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Choi W, Jeong H, Oh S, Jung TD. Instant gait classification for hip osteoarthritis patients: a non-wearable sensor approach utilizing Pearson correlation, SMAPE, and GMM. Biomed Eng Lett 2025; 15:301-310. [PMID: 40026883 PMCID: PMC11871253 DOI: 10.1007/s13534-024-00448-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 10/23/2024] [Accepted: 11/15/2024] [Indexed: 03/05/2025] Open
Abstract
This study aims to establish a methodology for classifying gait patterns in patients with hip osteoarthritis without the use of wearable sensors. Although patients with the same pathological condition may exhibit significantly different gait patterns, an accurate and efficient classification system is needed: one that reduces the effort and preparation time for both patients and clinicians, allowing gait analysis and classification without the need for cumbersome sensors like EMG or camera-based systems. The proposed methodology follows three key steps. First, ground reaction forces are measured in three directions-anterior-posterior, medial-lateral, and vertical-using a force plate during gait analysis. These force data are then evaluated through two approaches: trend similarity is assessed using the Pearson correlation coefficient, while scale similarity is measured with the Symmetric Mean Absolute Percentage Error (SMAPE), comparing results with healthy controls. Finally, Gaussian Mixture Models (GMM) are applied to cluster both healthy controls and patients, grouping the patients into distinct categories based on six quantified metrics derived from the correlation and SMAPE. Using the proposed methodology, 16 patients with hip osteoarthritis were successfully categorized into two distinct gait groups (Group 1 and Group 2). The gait patterns of these groups were further analyzed by comparing joint moments and angles in the lower limbs among healthy individuals and the classified patient groups. This study demonstrates that gait pattern classification can be reliably achieved using only force-plate data, offering a practical tool for personalized rehabilitation in hip osteoarthritis patients. By incorporating quantitative variables that capture both gait trends and scale, the methodology efficiently classifies patients with just 2-3 ms of natural walking. This minimizes the burden on patients while delivering a more accurate and realistic assessment. The proposed approach maintains a level of accuracy comparable to more complex methods, while being easier to implement and more accessible in clinical settings.
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Affiliation(s)
- Wiha Choi
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, 711-785 Republic of Korea
| | - Hieyong Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbongro, Bukgu, Gwangju, 61186 Republic of Korea
| | - Sehoon Oh
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, 711-785 Republic of Korea
| | - Tae-Du Jung
- School of Medicine, Kyungpook National University Hospital, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41404 Republic of Korea
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Smits Serena R, Hinterwimmer F, Burgkart R, von Eisenhart-Rothe R, Rueckert D. The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review. JMIR Mhealth Uhealth 2025; 13:e60521. [PMID: 39880389 PMCID: PMC11822330 DOI: 10.2196/60521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 10/20/2024] [Accepted: 11/12/2024] [Indexed: 01/31/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research. OBJECTIVE This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities. The focus will be on the types of models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research. METHODS This study examines this synergy of AI models and IMU data by using a systematic methodology, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to explore 3 core questions: (1) Which medical fields are most actively researching AI and IMU data? (2) Which models are being used in the analysis of IMU data within these medical fields? (3) What are the characteristics of the datasets used for in this fields? RESULTS The median dataset size is of 50 participants, which poses significant limitations for AI models given their dependency on large datasets for effective training and generalization. Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. In addition, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underapplication of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts. CONCLUSIONS In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research.
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Affiliation(s)
- Ricardo Smits Serena
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Florian Hinterwimmer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Rainer Burgkart
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rudiger von Eisenhart-Rothe
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
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3
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Kuroda T, Okamoto S, Akiyama Y. Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis. SENSORS (BASEL, SWITZERLAND) 2024; 24:7342. [PMID: 39599118 PMCID: PMC11598456 DOI: 10.3390/s24227342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2024] [Revised: 11/13/2024] [Accepted: 11/15/2024] [Indexed: 11/29/2024]
Abstract
Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment-the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability.
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Affiliation(s)
- Tomohito Kuroda
- Department of Computer Science, Tokyo Metropolitan University, 6-6, Asahigaoka, Hino 191-0065, Japan;
| | - Shogo Okamoto
- Department of Computer Science, Tokyo Metropolitan University, 6-6, Asahigaoka, Hino 191-0065, Japan;
| | - Yasuhiro Akiyama
- Faculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda 386-8567, Japan;
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Pang TY, Connelly T, Feltham F, Cheng CT, Rahman A, Chan J, McCarney L, Neville K. A Wearable Personalised Sonification and Biofeedback Device to Enhance Movement Awareness. SENSORS (BASEL, SWITZERLAND) 2024; 24:4814. [PMID: 39123861 PMCID: PMC11314819 DOI: 10.3390/s24154814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/29/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024]
Abstract
Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system's accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson's disease.
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Affiliation(s)
- Toh Yen Pang
- Biomedical Engineering, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia;
| | - Thomas Connelly
- Biomedical Engineering, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia;
| | - Frank Feltham
- Industrial Design, School of Design, College of Design and Social Context, RMIT University, Melbourne, VIC 3000, Australia;
| | - Chi-Tsun Cheng
- Mechanical, Manufacturing and Mechatronic Engineering, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia;
| | - Azizur Rahman
- Occupational Health and Safety/Ergonomics, Construction, School of Property, Construction and Project Management, Design and Social Context, RMIT University, Melbourne, VIC 3000, Australia;
| | - Jeffrey Chan
- Data Science & Artificial Intelligence, School of Computing Technologies, STEM College, RMIT University, Melbourne, VIC 3000, Australia;
| | - Luke McCarney
- Rehabilitation Sciences, School of Health and Biomedical Sciences, STEM College, RMIT University, Bundoora West, VIC 3083, Australia;
| | - Katrina Neville
- Electrical & Electronic Engineering, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia;
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Rukmini PG, Hegde RB, Basavarajappa BK, Bhat AK, Pujari AN, Gargiulo GD, Gunawardana U, Jan T, Naik GR. Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey. SENSORS (BASEL, SWITZERLAND) 2024; 24:4301. [PMID: 39001080 PMCID: PMC11243832 DOI: 10.3390/s24134301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/16/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.
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Affiliation(s)
- Pradyumna G. Rukmini
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Roopa B. Hegde
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Bommegowda K. Basavarajappa
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Anil Kumar Bhat
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Amit N. Pujari
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hertfordshire AL10 9AB, UK;
- School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
- The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Penrith, NSW 2751, Australia
- Translational Health Research Institute, Western Sydney University, Penrith, NSW 2751, Australia
- The Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
| | - Tony Jan
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
| | - Ganesh R. Naik
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
- College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
- Design and Creative Technology Vertical, Torrens University, Wakefield Street, Adelaide, SA 5000, Australia
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Santicchi G, Stillavato S, Deriu M, Comi A, Cerveri P, Esposito F, Zago M. Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:3343. [PMID: 38894136 PMCID: PMC11174549 DOI: 10.3390/s24113343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.
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Affiliation(s)
- Gabriele Santicchi
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Susanna Stillavato
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Marco Deriu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy; (M.D.); (P.C.)
| | - Aldo Comi
- Soccerment s.r.l, Viale Monza 259/265, 20126 Milan, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy; (M.D.); (P.C.)
| | - Fabio Esposito
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
| | - Matteo Zago
- Department of Biomedical Sciences for Health, Università Statale di Milano, Via Mangiagalli 31, 20133 Milan, Italy; (S.S.); (F.E.); (M.Z.)
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Shiwa T, Kawabata Y, Ishii T, Anan M. Effect of heel lift insertion on gait function in a patient with total hip arthroplasty with patient-perceived leg length difference: a case report. J Phys Ther Sci 2024; 36:81-86. [PMID: 38304151 PMCID: PMC10830159 DOI: 10.1589/jpts.36.81] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/02/2023] [Indexed: 02/03/2024] Open
Abstract
[Purpose] The effect of heel lift insertion on gait in patients who have undergone total hip arthroplasty (THA) with patient-perceived leg length difference is seldom referenced in the literature. We used an AB design to investigate the alterations of gait function before and after inserting a heel lift on the non-operative side. [Participant and Methods] The participant had a patient-perceived leg length difference after THA and presented with gait disturbance. The survey phase was 10 days (phase A: normal physiotherapy for five days, and phase B: normal physiotherapy and heel lift insertion for another five days) from the 17th day following THA. The ambulatory task was conducted at a self-determined, comfortable pace and objectively assessed using an inertial sensor. [Results] The insertion of a heel lift partially improved the gait symmetry and the ratio of lumbar acceleration in three directions; it also corrected the patient-perceived leg length difference. [Conclusion] An investigation was carried out to examine the impact of a heel lift on gait in a single case of THA with patient-reported leg length difference. The application of a heel lift may enhance the relationship between the patient-perceived leg length difference, gait symmetry, and the ratio of lumbar acceleration in three dimensions.
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Affiliation(s)
- Toru Shiwa
- Graduate School of Welfare Health Science, Oita University,
Japan
- Department of Rehabilitation, Soejima Orthopedic Hospital,
Japan
| | - Yuji Kawabata
- Department of Rehabilitation Medicine, Shuto General
Hospital, JA Yamaguchi Prefectural Welfare Federation of Agricultural Cooperative,
Japan
| | - Takako Ishii
- Department of Orthopedic, Soejima Orthopedic Hospital,
Japan
| | - Masaya Anan
- Physical Therapy Course, Faculty of Welfare and Health
Science, Oita University: 700 Dannoharu, Oita-shi, Oita 870-1192, Japan
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He Y, Chen Y, Tang L, Chen J, Tang J, Yang X, Su S, Zhao C, Xiao N. Accuracy validation of a wearable IMU-based gait analysis in healthy female. BMC Sports Sci Med Rehabil 2024; 16:2. [PMID: 38167148 PMCID: PMC10762813 DOI: 10.1186/s13102-023-00792-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system. METHODS In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems. RESULTS The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable. CONCLUSIONS The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.
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Affiliation(s)
- Yi He
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yuxia Chen
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jing Chen
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Jing Tang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Xiaoxuan Yang
- Shanqi (Chongqing) Smart Medical Technology Co., Ltd., Chongqing, China
| | - Songchuan Su
- Chongqing Orthopedics Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Chen Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
| | - Nong Xiao
- Department of Rehabilitation, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, 400016, China.
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Ji CY, Yang LR. Perioperative nursing care for hip arthroplasty patients with concomitant hypertension: A minireview. World J Clin Cases 2023; 11:8440-8446. [PMID: 38188203 PMCID: PMC10768512 DOI: 10.12998/wjcc.v11.i36.8440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/18/2023] [Accepted: 12/08/2023] [Indexed: 12/22/2023] Open
Abstract
Hip replacement (HA) is mainly indicated for the elderly, who generally suffer from various underlying diseases such as hypertension. This article provides a review of the key points of perioperative nursing care for patients with hypertension undergoing HA. It analyzes the key points of care during the perioperative period (preoperative, intraoperative, and postoperative) and proposes directions for the development of perioperative nursing care for HA. The prognosis for patients can be improved through the modification of traditional medical approaches and the application of new technologies and concepts.
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Affiliation(s)
- Chang-Yue Ji
- Department of Orthopedic Sports Medicine Joint Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Li-Ru Yang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
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10
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Liu R, Liu L, Ma G, Feng S, Mu Y, Meng D, Wang S, Cai E. Visual Gait Analysis Based on UE4. SENSORS (BASEL, SWITZERLAND) 2023; 23:5463. [PMID: 37420630 DOI: 10.3390/s23125463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 07/09/2023]
Abstract
With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body's structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body's motion posture, which has great application prospects in the field of gait analysis.
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Affiliation(s)
- Ruzhang Liu
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Luyin Liu
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Guochao Ma
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Shanshan Feng
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Yuanhui Mu
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Dexi Meng
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Shuying Wang
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
| | - Enlin Cai
- The School of Electronic Information, Qingdao University, Qingdao 266071, China
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11
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Acosta-Vargas P, Flor O, Salvador-Acosta B, Suárez-Carreño F, Santórum M, Solorzano S, Salvador-Ullauri L. Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5048. [PMID: 37299775 PMCID: PMC10255108 DOI: 10.3390/s23115048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/08/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
The objective of this scoping review is to characterize the current panorama of inertia sensors for the rehabilitation of hip arthroplasty. In this context, the most widely used sensors are IMUs, which combine accelerometers and gyroscopes to measure acceleration and angular velocity in three axes. We found that data collected by the IMU sensors are used to analyze and detect any deviation from the normal to measure the position and movement of the hip joint. The main functions of inertial sensors are to measure various aspects of training, such as speed, acceleration, and body orientation. The reviewers extracted the most relevant articles published between 2010 and 2023 in the ACM Digital Library, PubMed, ScienceDirect, Scopus, and Web of Science. In this scoping review, the PRISMA-ScR checklist was used, and a Cohen's kappa coefficient of 0.4866 was applied, implying moderate agreement between reviewers; 23 primary studies were extracted from a total of 681. In the future, it will be an excellent challenge for experts in inertial sensors with medical applications to provide access codes for other researchers, which will be one of the most critical trends in the advancement of applications of portable inertial sensors for biomechanics.
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Affiliation(s)
- Patricia Acosta-Vargas
- Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador; (O.F.); (F.S.-C.)
- Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador; (B.S.-A.); (S.S.)
- Facultad de Tecnologías de Información, Universidad Latina de Costa Rica, San José 11501, Costa Rica
| | - Omar Flor
- Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador; (O.F.); (F.S.-C.)
| | - Belén Salvador-Acosta
- Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador; (B.S.-A.); (S.S.)
- Facultad de Medicina, Universidad de Las Américas, Quito 170125, Ecuador
| | - Franyelit Suárez-Carreño
- Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador; (O.F.); (F.S.-C.)
| | - Marco Santórum
- Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador;
| | - Santiago Solorzano
- Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador; (B.S.-A.); (S.S.)
| | - Luis Salvador-Ullauri
- Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain;
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12
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Dindorf C, Ludwig O, Simon S, Becker S, Fröhlich M. Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture Parameters. Bioengineering (Basel) 2023; 10:bioengineering10050511. [PMID: 37237581 DOI: 10.3390/bioengineering10050511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/21/2023] [Accepted: 04/22/2023] [Indexed: 05/28/2023] Open
Abstract
Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are, therefore, often subjective and prone to errors. Machine learning (ML) methods in combination with explainable artificial intelligence (XAI) tools have proven useful for providing an objective, data-based orientation. However, only a few works have considered posture parameters, leaving the potential for more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). The posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. The label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (MPRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible, in general. In the context of personalized medicine, the present study's approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.
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Affiliation(s)
- Carlo Dindorf
- Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Oliver Ludwig
- Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Steven Simon
- Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Stephan Becker
- Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Michael Fröhlich
- Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
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13
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Kaufmann M, Nüesch C, Clauss M, Pagenstert G, Eckardt A, Ilchmann T, Stoffel K, Mündermann A, Ismailidis P. Functional assessment of total hip arthroplasty using inertial measurement units: Improvement in gait kinematics and association with patient-reported outcome measures. J Orthop Res 2023; 41:759-770. [PMID: 35880355 DOI: 10.1002/jor.25421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 07/08/2022] [Accepted: 07/23/2022] [Indexed: 02/04/2023]
Abstract
Inertial measurement units (IMUs) are commonly used for gait assessment, yet their potential for quantifying improvements in gait function and patterns after total hip arthroplasty (THA) has not been fully explored. The primary aim of this study was to compare spatiotemporal parameters and sagittal plane kinematic patterns of patients with hip osteoarthritis (OA) before and after THA, and to asymptomatic controls. The secondary aim was to assess the association between dynamic hip range of motion (ROM) during walking and the Hip Osteoarthritis Outcome Scores (HOOS). Twenty-four patients with hip OA and 24 matched asymptomatic controls completed gait analyses using the RehaGait® sensor system. Patients were evaluated pre- and 1 year postoperatively, controls in a single visit. Differences in kinematic data were analyzed using statistical parametric mapping, and correlations between dynamic hip ROM and HOOS were calculated. Walking speed and stride length significantly increased (+0.08 m/s, p = 0.019; +0.06 m, p = 0.048) after THA but did not reach the level of asymptomatic controls (-0.11 m/s, p = 0.028; -0.14 m, p = 0.001). Preoperative hip and knee kinematics differed significantly from controls. After THA, they improved significantly and did not differ from controls. Dynamic hip flexion-extension ROM correlated positively with all HOOS subscores (r > 0.417; p ≤ 0.001). The change in HOOS symptoms in patients was explained by the combination of baseline HOOS symptoms and change in dynamic hip ROM (r2 = 0.748) suggesting that the additional information gained with IMU gait analysis helps to complement and objectify patient-reported outcome measures pre- and postoperatively and monitor treatment-related improvements.
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Affiliation(s)
- Mara Kaufmann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland.,Department of Clinical Research, University of Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
| | - Martin Clauss
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland.,Center for Musculoskeletal Infections, University Hospital Basel, Basel, Switzerland
| | - Geert Pagenstert
- Department of Clinical Research, University of Basel, Basel, Switzerland.,Clarahof Clinic of Orthopaedic Surgery, Basel, Switzerland
| | - Anke Eckardt
- ENDO-Team, Hirslanden Klinik, Birshof, Münchenstein, Switzerland
| | - Thomas Ilchmann
- ENDO-Team, Hirslanden Klinik, Birshof, Münchenstein, Switzerland
| | - Karl Stoffel
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland.,Department of Clinical Research, University of Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
| | - Petros Ismailidis
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland.,Department of Clinical Research, University of Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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14
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Li Z, Maimaiti Z, Fu J, Chen JY, Xu C. Global research landscape on artificial intelligence in arthroplasty: A bibliometric analysis. Digit Health 2023; 9:20552076231184048. [PMID: 37361434 PMCID: PMC10286212 DOI: 10.1177/20552076231184048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has promising applications in arthroplasty. In response to the knowledge explosion resulting from the rapid growth of publications, we applied bibliometric analysis to explore the research profile and topical trends in this field. METHODS The articles and reviews related to AI in arthroplasty were retrieved from 2000 to 2021. The Java-based Citespace, VOSviewer, R software-based Bibiometrix, and an online platform systematically evaluated publications by countries, institutions, authors, journals, references, and keywords. RESULTS A total of 867 publications were included. Over the past 22 years, the number of AI-related publications in the field of arthroplasty has grown exponentially. The United States was the most productive and academically influential country. The Cleveland Clinic was the most prolific institution. Most publications were published in high academic impact journals. However, collaborative networks revealed a lack and imbalance of inter-regional, inter-institutional, and inter-author cooperation. Two emerging research areas represented the development trends: major AI subfields such as machine learning and deep learning, and the other is research related to clinical outcomes. CONCLUSION AI in arthroplasty is evolving rapidly. Collaboration between different regions and institutions should be strengthened to deepen our understanding further and exert critical implications for decision-making. Predicting clinical outcomes of arthroplasty using novel AI strategies may be a promising application in this field.
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Affiliation(s)
- Zhuo Li
- School of Medicine, Nankai University, Tianjin, People's Republic of China
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Zulipikaer Maimaiti
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Jun Fu
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Ji-Ying Chen
- School of Medicine, Nankai University, Tianjin, People's Republic of China
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Chi Xu
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
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15
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Gao Z, Zhu Y, Fang Y, Fekete G, Kovács A, Baker JS, Liang M, Gu Y. Automated recognition of asymmetric gait and fatigue gait using ground reaction force data. Front Physiol 2023; 14:1159668. [PMID: 36960154 PMCID: PMC10027919 DOI: 10.3389/fphys.2023.1159668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 02/23/2023] [Indexed: 03/09/2023] Open
Abstract
Introduction: The purpose of this study was to evaluate the effect of running-induced fatigue on the characteristic asymmetry of running gait and to identify non-linear differences in bilateral lower limbs and fatigued gait by building a machine learning model. Methods: Data on bilateral lower limb three-dimensional ground reaction forces were collected from 14 male amateur runners before and after a running-induced fatigue experiment. The symmetry function (SF) was used to assess the degree of symmetry of running gait. Statistical parameter mapping (Paired sample T-test) algorithm was used to examine bilateral lower limb differences and asymmetry changes pre- and post-fatigue of time series data. The support vector ma-chine (SVM) algorithm was used to recognize the gait characteristics of both lower limbs before and after fatigue and to build the optimal algorithm model by setting different kernel functions. Results: The results showed that the ground reaction forces were asymmetrical (SF > 0.5) both pre-and post-fatigue and mainly concentrated in the medial-lateral direction. The asymmetry of the medial-lateral direction increased significantly after fatigue (p < 0.05). In addition, we concluded that the polynomial kernel function could make the SVM model the most accurate in classifying left and right gait features (accuracy of 85.3%, 82.4%, and 82.4% in medial-lateral, anterior-posterior and vertical directions, respectively). Gaussian radial basis kernel function was the optimal kernel function of the SVM algorithm model for fatigue gait recognition in the medial-lateral and vertical directions (accuracy of 54.2% and 62.5%, respectively). Moreover, polynomial was the optimal kernel function of the anterior-posterior di-rection (accuracy = 54.2%). Discussion: We proved in this study that the SVM algorithm model depicted good performance in identifying asymmetric and fatigue gaits. These findings can provide implications for running injury prevention, movement monitoring, and gait assessment.
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Affiliation(s)
- Zixiang Gao
- Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China
- Faculty of Engineering, University of Pannonia, Veszprém, Hungary
- Savaria Institute of Technology, Eötvös Loránd University, Szombathely, Hungary
| | - Yining Zhu
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Yufei Fang
- Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China
| | - Gusztáv Fekete
- Savaria Institute of Technology, Eötvös Loránd University, Szombathely, Hungary
| | - András Kovács
- Faculty of Engineering, University of Pannonia, Veszprém, Hungary
| | - Julien S. Baker
- Department of Sport and Physical Education, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Minjun Liang
- Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China
- Department of Physical and Health Education, Udon Thani Rajabhat University, Udon Thani, Thailand
- *Correspondence: Minjun Liang, ; Yaodong Gu,
| | - Yaodong Gu
- Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China
- *Correspondence: Minjun Liang, ; Yaodong Gu,
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16
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Polisetty TS, Jain S, Pang M, Karnuta JM, Vigdorchik JM, Nawabi DH, Wyles CC, Ramkumar PN. Concerns surrounding application of artificial intelligence in hip and knee arthroplasty. Bone Joint J 2022; 104-B:1292-1303. [DOI: 10.1302/0301-620x.104b12.bjj-2022-0922.r1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303.
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Affiliation(s)
- Teja S. Polisetty
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samagra Jain
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Michael Pang
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jaret M. Karnuta
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Danyal H. Nawabi
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| | - Cody C. Wyles
- Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Prem N. Ramkumar
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
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17
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Linnhoff D, Ploigt R, Mattes K. Sofigait-A Wireless Inertial Sensor-Based Gait Sonification System. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228782. [PMID: 36433376 PMCID: PMC9698922 DOI: 10.3390/s22228782] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 05/14/2023]
Abstract
In this study, a prototype of an inertial sensor-based gait sonification system was tested for the purpose of providing real-time gait feedback on the knee angle. The study consisted of two parts: (1) a comparison of the knee angle measurement to a marker-based 3D optical capturing system (Vicon, Oxford, UK) with N = 24 participants and (2) an evaluation four different sonification feedback versions in an accentuation × pitch (2 × 2) design on a sample of N = 28 participants. For the measurement system comparison, the RMSE was 7.6° ± 2.6° for the left and 6.9° ± 3.1° for the right side. Measurement agreement with bias up to −7.5° ± 6.2° (for maximum knee flexion) was indicated by the Bland−Altmann Method. The SPM revealed significant differences between both measurement systems for the area 45−90% (p < 0.001) (left) and the area between 45% and 80% (p = 0.007) (right). For the sonification perception, the variation of pitch had a significant effect on the perception of pleasantness of the sound. No effect was found for the accentuation of the swing or stance phase.
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Affiliation(s)
- Dagmar Linnhoff
- Institute of Human Movement Science, University of Hamburg, 20148 Hamburg, Germany
- Correspondence:
| | | | - Klaus Mattes
- Institute of Human Movement Science, University of Hamburg, 20148 Hamburg, Germany
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18
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. SENSORS (BASEL, SWITZERLAND) 2022; 22:8398. [PMID: 36366096 PMCID: PMC9654083 DOI: 10.3390/s22218398] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.
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Affiliation(s)
- Michael V. Potter
- Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USA
| | - Stephen M. Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Reed D. Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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19
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Emmerzaal J, Van Rossom S, van der Straaten R, De Brabandere A, Corten K, De Baets L, Davis J, Jonkers I, Timmermans A, Vanwanseele B. Joint kinematics alone can distinguish hip or knee osteoarthritis patients from asymptomatic controls with high accuracy. J Orthop Res 2022; 40:2229-2239. [PMID: 35043466 DOI: 10.1002/jor.25269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/17/2021] [Accepted: 01/06/2022] [Indexed: 02/04/2023]
Abstract
Osteoarthritis (OA) is one of the leading musculoskeletal disabilities worldwide, and several interventions intend to change the gait pattern in OA patients to more healthy patterns. However, an accessible way to follow up the biomechanical changes in a clinical setting is still missing. Therefore, this study aims to evaluate whether we can use biomechanical data collected from a specific activity of daily living to help distinguish hip OA patients from controls and knee OA patients from controls using features that potentially could be measured in a clinical setting. To achieve this goal, we considered three different classes of statistical models with different levels of data complexity. Class 1 is kinematics based only (clinically applicable), class 2 includes joint kinetics (semi-applicable under the condition of access to a force plate or prediction models), and class 3 uses data from advanced musculoskeletal modeling (not clinically applicable). We used a machine learning pipeline to determine which classification model was best. We found 100% classification accuracy for KneeOA-vs-Asymptomatic and 93.9% for HipOA-vs-Asymptomatic using seven features derived from the lumbar spine and hip kinematics collected during ascending stairs. These results indicate that kinematical data alone can distinguish hip or knee OA patients from asymptomatic controls. However, to enable clinical use, we need to validate if the classifier also works with sensor-based kinematical data and whether the probabilistic outcome of the logistic regression model can be used in the follow-up of patients with OA.
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Affiliation(s)
- Jill Emmerzaal
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Belgium.,REVAL Rehabilitation Research Centre, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium
| | - Sam Van Rossom
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Belgium
| | - Rob van der Straaten
- REVAL Rehabilitation Research Centre, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium
| | - Arne De Brabandere
- Declarative Languages and Artificial Intelligence Group, Department of Computer Science, KU Leuven, Belgium
| | - Kristoff Corten
- Department of Orthopaedics, Ziekenhuis Oost Limburg, Genk, Belgium
| | - Liesbet De Baets
- Pain in Motion Research Group (PAIN), Department of Physiotherapy, Human Physiology and Anatomy, Vrije Universiteit Brussel, Ixelles, Belgium
| | - Jesse Davis
- Declarative Languages and Artificial Intelligence Group, Department of Computer Science, KU Leuven, Belgium
| | - Ilse Jonkers
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Belgium
| | - Annick Timmermans
- REVAL Rehabilitation Research Centre, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium
| | - Benedicte Vanwanseele
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Belgium
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20
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Mitternacht J, Hermann A, Carqueville P. Acquisition of Lower-Limb Motion Characteristics with a Single Inertial Measurement Unit—Validation for Use in Physiotherapy. Diagnostics (Basel) 2022; 12:diagnostics12071640. [PMID: 35885542 PMCID: PMC9317307 DOI: 10.3390/diagnostics12071640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/21/2022] [Accepted: 07/02/2022] [Indexed: 11/16/2022] Open
Abstract
In physiotherapy, there is still a lack of practical measurement options to track the progress of therapy or rehabilitation following injuries to the lower limbs objectively and reproducibly yet simply and with minimal effort and time. We aim at filling this gap with the design of an IMU (inertial measurement unit) system with only one sensor placed on the tibia edge. In our study, the IMU system evaluated a set of 10 motion tests by a score value for each test and stored them in a database for a more reliable longitudinal assessment of the progress. The sensor analyzed the different motion patterns and obtained characteristic physiological parameters, such as angle ranges, and spatial and angular displacements, such as knee valgus under load. The scores represent the patient’s coordination, stability, strength and speed. To validate the IMU system, these scores were compared to corresponding values from a simultaneously recorded marker-based 3D video motion analysis of the measurements from five healthy volunteers. Score differences between the two systems were almost always within 1–3 degrees for angle measurements. Timing-related measurements were nearly completely identical. The tests on the valgus stability of the knee showed equally small deviations but should nevertheless be repeated with patients, because the healthy subjects showed no signs of instability.
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21
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Bo F, Yerebakan M, Dai Y, Wang W, Li J, Hu B, Gao S. IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review. Healthcare (Basel) 2022; 10:healthcare10071210. [PMID: 35885736 PMCID: PMC9318359 DOI: 10.3390/healthcare10071210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 01/22/2023] Open
Abstract
With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.
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Affiliation(s)
- Fan Bo
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mustafa Yerebakan
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;
| | - Yanning Dai
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China;
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
| | - Weibing Wang
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia Li
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.L.); (B.H.); (S.G.)
| | - Boyi Hu
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;
- Correspondence: (J.L.); (B.H.); (S.G.)
| | - Shuo Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China;
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
- Correspondence: (J.L.); (B.H.); (S.G.)
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22
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Polce EM, Kunze KN, Dooley MS, Piuzzi NS, Boettner F, Sculco PK. Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting. J Bone Joint Surg Am 2022; 104:821-832. [PMID: 35045061 DOI: 10.2106/jbjs.21.00717] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. METHODS PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. RESULTS Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). CONCLUSIONS The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.
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Affiliation(s)
- Evan M Polce
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Matthew S Dooley
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio
| | - Friedrich Boettner
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
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23
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Das R, Paul S, Mourya GK, Kumar N, Hussain M. Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait. Front Neurosci 2022; 16:859298. [PMID: 35495059 PMCID: PMC9051393 DOI: 10.3389/fnins.2022.859298] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/01/2022] [Indexed: 12/06/2022] Open
Abstract
The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.
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Affiliation(s)
- Ratan Das
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Gajendra Kumar Mourya
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Neelesh Kumar
- Biomedical Applications Unit, Central Scientific Instruments Organisation, Chandigarh, India
| | - Masaraf Hussain
- Department of Neurology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
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24
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Kim JK, Bae MN, Lee K, Kim JC, Hong SG. Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life. BIOSENSORS 2022; 12:bios12030167. [PMID: 35323437 PMCID: PMC8946270 DOI: 10.3390/bios12030167] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 12/11/2022]
Abstract
Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.
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Affiliation(s)
- Jeong-Kyun Kim
- Department of Computer Software, University of Science and Technology, Daejeon 34113, Korea;
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.L.); (J.-C.K.)
| | - Myung-Nam Bae
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.L.); (J.-C.K.)
| | - Kangbok Lee
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.L.); (J.-C.K.)
| | - Jae-Chul Kim
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.L.); (J.-C.K.)
| | - Sang Gi Hong
- Department of Computer Software, University of Science and Technology, Daejeon 34113, Korea;
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.L.); (J.-C.K.)
- Correspondence: ; Tel.: +82-42-860-1795
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25
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Jansen CP, Gordt-Oesterwind K, Schwenk M. Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research. SENSORS 2022; 22:s22030973. [PMID: 35161719 PMCID: PMC8837998 DOI: 10.3390/s22030973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/21/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Carl-Philipp Jansen
- Department of Clinical Gerontology and Geriatric Rehabilitation, Robert Bosch Hospital, 70376 Stuttgart, Germany
- Institute of Sports and Sports Sciences, Heidelberg University, 69120 Heidelberg, Germany;
- Correspondence:
| | | | - Michael Schwenk
- Network Aging Research (NAR), Heidelberg University, 69115 Heidelberg, Germany;
- Human Performance Research Centre, Department of Sport Science, University of Konstanz, 78457 Konstanz, Germany
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26
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Kunze KN, Orr M, Krebs V, Bhandari M, Piuzzi NS. Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications. Bone Jt Open 2022; 3:93-97. [PMID: 35084227 PMCID: PMC9047073 DOI: 10.1302/2633-1462.31.bjo-2021-0123.r1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Melissa Orr
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Viktor Krebs
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mohit Bhandari
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Department of Surgery, Division of Orthopaedic Surgery, McMaster University, Cleveland, Ohio, USA
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
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27
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LANDA-JIMÉNEZ MIGUELA, GONZÁLEZ-GASPAR PATRICIA, MONTES-GONZÁLEZ FERNANDOM, MORGADO-VALLE CONSUELO, BELTRÁN-PARRAZAL LUIS. An open-source low-cost wireless sensor system for acquisition of human movement data. AN ACAD BRAS CIENC 2022; 94:e20191419. [DOI: 10.1590/0001-3765202220191419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 12/03/2020] [Indexed: 11/22/2022] Open
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28
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Teufl W, Taetz B, Miezal M, Dindorf C, Fröhlich M, Trinler U, Hogan A, Bleser G. Automated detection and explainability of pathological gait patterns using a one-class support vector machine trained on inertial measurement unit based gait data. Clin Biomech (Bristol, Avon) 2021; 89:105452. [PMID: 34481198 DOI: 10.1016/j.clinbiomech.2021.105452] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Machine learning approaches for the classification of pathological gait based on kinematic data, e.g. derived from inertial sensors, are commonly used in terms of a multi-class classification problem. However, there is a lack of research regarding one-class classifiers that are independent of certain pathologies. Therefore, it was the aim of this work to design a one-class classifier based on healthy norm-data that provides not only a prediction probability but rather an explanation of the classification decision, increasing the acceptance of this machine learning approach. METHODS The inertial sensor based gait kinematics of 25 healthy subjects was employed to train a one-class support vector machine. 25 healthy subjects, 20 patients after total hip arthroplasty and one transfemoral amputee served to validate the classifier. Prediction probabilities and feature importance scores were estimated for each subject. FINDINGS The support vector machine predicted 100% of the patients as outliers from the healthy group. Three healthy subjects were predicted as outliers. The feature importance calculation revealed the hip in the sagittal plane as most relevant feature concerning the group after total hip arthroplasty. For the misclassified healthy subject with the lowest probability score the knee flexion and the pelvis obliquity were identified. INTERPRETATION The support vector machine seems a useful tool to identify outliers from a healthy norm-group. The feature importance examination proved to provide valuable information on the musculoskeletal status of the subjects. In this combination, the present approach could be employed in various disciplines to identify abnormal gait and suggest subsequent training.
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Affiliation(s)
- Wolfgang Teufl
- University of Salzburg, Department of Sport Science, Schlossallee 49, 5400 Hallein, Austria.
| | - Bertram Taetz
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
| | - Markus Miezal
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
| | - Carlo Dindorf
- Technische Universität Kaiserslautern, Department of Sport Science, Erwin-Schrödinger-Straße 57, 67663 Kaiserslautern, Germany.
| | - Michael Fröhlich
- Technische Universität Kaiserslautern, Department of Sport Science, Erwin-Schrödinger-Straße 57, 67663 Kaiserslautern, Germany.
| | - Ursula Trinler
- BG Klinik Ludwigshafen, Ludwig-Guttmann-Straße 13, 67071 Ludwigshafen, Germany.
| | - Aidan Hogan
- BG Klinik Ludwigshafen, Ludwig-Guttmann-Straße 13, 67071 Ludwigshafen, Germany.
| | - Gabriele Bleser
- Technische Universität Kaiserslautern, Department of Computer Science, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany.
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29
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Dindorf C, Konradi J, Wolf C, Taetz B, Bleser G, Huthwelker J, Werthmann F, Bartaguiz E, Kniepert J, Drees P, Betz U, Fröhlich M. Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI). SENSORS (BASEL, SWITZERLAND) 2021; 21:6323. [PMID: 34577530 PMCID: PMC8470313 DOI: 10.3390/s21186323] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 01/10/2023]
Abstract
Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt's method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany; (E.B.); (M.F.)
| | - Jürgen Konradi
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (J.K.); (C.W.); (J.H.); (U.B.)
| | - Claudia Wolf
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (J.K.); (C.W.); (J.H.); (U.B.)
| | - Bertram Taetz
- Department Augmented Vision, German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; (B.T.); (G.B.)
| | - Gabriele Bleser
- Department Augmented Vision, German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; (B.T.); (G.B.)
| | - Janine Huthwelker
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (J.K.); (C.W.); (J.H.); (U.B.)
| | - Friederike Werthmann
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (F.W.); (J.K.); (P.D.)
| | - Eva Bartaguiz
- Department of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany; (E.B.); (M.F.)
| | - Johanna Kniepert
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (F.W.); (J.K.); (P.D.)
| | - Philipp Drees
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (F.W.); (J.K.); (P.D.)
| | - Ulrich Betz
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany; (J.K.); (C.W.); (J.H.); (U.B.)
| | - Michael Fröhlich
- Department of Sports Science, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany; (E.B.); (M.F.)
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30
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Rose MJ, Costello KE, Eigenbrot S, Torabian K, Kumar D. Inertial measurement units and application for remote healthcare in hip and knee osteoarthritis: a narrative review (Preprint). JMIR Rehabil Assist Technol 2021; 9:e33521. [PMID: 35653180 PMCID: PMC9204569 DOI: 10.2196/33521] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/18/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions. Objective This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population. Methods A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review. Results From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment. Conclusions Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.
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Affiliation(s)
- Michael J Rose
- Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States
| | - Kerry E Costello
- Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States
- Division of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Samantha Eigenbrot
- Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States
| | - Kaveh Torabian
- Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States
| | - Deepak Kumar
- Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States
- Division of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
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31
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Nüesch C, Ismailidis P, Koch D, Pagenstert G, Ilchmann T, Eckardt A, Stoffel K, Egloff C, Mündermann A. Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis. SENSORS 2021; 21:s21165363. [PMID: 34450828 PMCID: PMC8398113 DOI: 10.3390/s21165363] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 12/13/2022]
Abstract
There is a great need for quantitative outcomes reflecting the functional status in patients with knee or hip osteoarthritis (OA) to advance the development and investigation of interventions for OA. The purpose of this study was to determine if gait kinematics specific to the disease—i.e., knee versus hip OA—can be identified using wearable sensors and statistical parametric mapping (SPM) and whether disease-related gait deviations are associated with patient reported outcome measures. 113 participants (N = 29 unilateral knee OA; N = 30 unilateral hip OA; N = 54 age-matched asymptomatic persons) completed gait analysis with wearable sensors and the Knee/Hip Osteoarthritis Outcome Score (KOOS/HOOS). Data were analyzed using SPM. Knee and hip kinematics differed between patients with knee OA and patients with hip OA (up to 14°, p < 0.001 for knee and 8°, p = 0.003 for hip kinematics), and differences from controls were more pronounced in the affected than unaffected leg of patients. The observed deviations in ankle, knee and hip kinematic trajectories from controls were associated with KOOS/HOOS in both groups. Capturing gait kinematics using wearables has a large potential for application as outcome in clinical trials and for monitoring treatment success in patients with knee or hip OA and in large cohorts representing a major advancement in research on musculoskeletal diseases.
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Affiliation(s)
- Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Spine Surgery, University Hospital Basel, 4031 Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
| | - Petros Ismailidis
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
| | - David Koch
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department for Sport, Movement and Health, University of Basel, 4052 Basel, Switzerland
| | - Geert Pagenstert
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
- Clarahof Clinic of Orthopaedic Surgery, 4058 Basel, Switzerland
| | - Thomas Ilchmann
- ENDO-Team, Hirslanden Klinik Birshof, 4142 Münchenstein, Switzerland; (T.I.); (A.E.)
| | - Anke Eckardt
- ENDO-Team, Hirslanden Klinik Birshof, 4142 Münchenstein, Switzerland; (T.I.); (A.E.)
| | - Karl Stoffel
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
| | - Christian Egloff
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Spine Surgery, University Hospital Basel, 4031 Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
- Correspondence:
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The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. SENSORS 2021; 21:s21144808. [PMID: 34300546 PMCID: PMC8309920 DOI: 10.3390/s21144808] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/05/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022]
Abstract
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
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Feature extraction and gait classification in hip replacement patients on the basis of kinematic waveform data. BIOMEDICAL HUMAN KINETICS 2021. [DOI: 10.2478/bhk-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Study aim: To find out, without relying on gait-specific assumptions or prior knowledge, which parameters are most important for the description of asymmetrical gait in patients after total hip arthroplasty (THA).
Material and methods: The gait of 22 patients after THA was recorded using an optical motion capture system. The waveform data of the marker positions, velocities, and accelerations, as well as joint and segment angles, were used as initial features. The random forest (RF) and minimum-redundancy maximum-relevance (mRMR) algorithms were chosen for feature selection. The results were compared with those obtained from the use of different dimensionality reduction methods.
Results: Hip movement in the sagittal plane, knee kinematics in the frontal and sagittal planes, marker position data of the anterior and posterior superior iliac spine, and acceleration data for markers placed at the proximal end of the fibula are highly important for classification (accuracy: 91.09%). With feature selection, better results were obtained compared to dimensionality reduction.
Conclusion: The proposed approaches can be used to identify and individually address abnormal gait patterns during the rehabilitation process via waveform data. The results indicate that position and acceleration data also provide significant information for this task.
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Kim JK, Bae MN, Lee KB, Hong SG. Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors. SENSORS 2021; 21:s21051786. [PMID: 33806525 PMCID: PMC7961754 DOI: 10.3390/s21051786] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022]
Abstract
Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.
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Affiliation(s)
- Jeong-Kyun Kim
- Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea;
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.B.L.)
| | - Myung-Nam Bae
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.B.L.)
| | - Kang Bok Lee
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.B.L.)
| | - Sang Gi Hong
- Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea;
- Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (M.-N.B.); (K.B.L.)
- Correspondence: ; Tel.: +82-42-860-1795
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Astephen Wilson JL, Kobsar D. Osteoarthritis year in review 2020: mechanics. Osteoarthritis Cartilage 2021; 29:161-169. [PMID: 33421562 DOI: 10.1016/j.joca.2020.12.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 12/02/2020] [Accepted: 12/21/2020] [Indexed: 02/06/2023]
Abstract
The mechanical environment of the joint during dynamic activity plays a significant role in osteoarthritis processes. Understanding how the magnitude, pattern and duration of joint-specific loading features contribute to osteoarthritis progression and response to treatment is a topic of on-going relevance. This narrative review synthesizes evidence from recent papers that have contributed to knowledge related to three identified emerging subthemes: 1) the role of the joint mechanical environment in osteoarthritis pathogenesis, 2) joint biomechanics as an outcome to arthroplasty treatment of osteoarthritis, and 3) methodological trends for advancing our knowledge of the role of biomechanics in osteoarthritis. Rather than provide an exhaustive review of a broad area of research, we have focused on evidence this year related to these subthemes. New research this year has indicated significant interest in using biomechanics investigations to understand structural vs clinical progression of osteoarthritis, the role and interaction in the three-dimensional loading environment of the joint, and the contribution of muscle activation and forces to osteoarthritis progression. There is ongoing interest in understanding how patient variability with respect to gait biomechanics influences arthroplasty surgery outcomes, and subgroup analyses have provided evidence for the potential utility in tailored treatment approaches. Finally, we are seeing a growing trend in the application of translational biomechanics tools such as wearable inertial measurement units for improved integration of biomechanics into clinical decision-making and outcomes assessment for osteoarthritis.
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Affiliation(s)
- J L Astephen Wilson
- Department of Surgery, McMaster University, 1280 Main St West, Hamilton, ON, Canada.
| | - D Kobsar
- Department of Kinesiology, McMaster University, 1280 Main St West, Hamilton, ON, Canada.
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Jayakumar P, Lin E, Galea V, Mathew AJ, Panda N, Vetter I, Haynes AB. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. J Pers Med 2020; 10:E282. [PMID: 33333915 PMCID: PMC7765378 DOI: 10.3390/jpm10040282] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/08/2020] [Accepted: 12/11/2020] [Indexed: 12/13/2022] Open
Abstract
Digital phenotyping-the moment-by-moment quantification of human phenotypes in situ using data related to activity, behavior, and communications, from personal digital devices, such as smart phones and wearables-has been gaining interest. Personalized health information captured within free-living settings using such technologies may better enable the application of patient-generated health data (PGHD) to provide patient-centered care. The primary objective of this scoping review is to characterize the application of digital phenotyping and digitally captured active and passive PGHD for outcome measurement in surgical care. Secondarily, we synthesize the body of evidence to define specific areas for further work. We performed a systematic search of four bibliographic databases using terms related to "digital phenotyping and PGHD," "outcome measurement," and "surgical care" with no date limits. We registered the study (Open Science Framework), followed strict inclusion/exclusion criteria, performed screening, extraction, and synthesis of results in line with the PRISMA Extension for Scoping Reviews. A total of 224 studies were included. Published studies have accelerated in the last 5 years, originating in 29 countries (mostly from the USA, n = 74, 33%), featuring original prospective work (n = 149, 66%). Studies spanned 14 specialties, most commonly orthopedic surgery (n = 129, 58%), and had a postoperative focus (n = 210, 94%). Most of the work involved research-grade wearables (n = 130, 58%), prioritizing the capture of activity (n = 165, 74%) and biometric data (n = 100, 45%), with a view to providing a tracking/monitoring function (n = 115, 51%) for the management of surgical patients. Opportunities exist for further work across surgical specialties involving smartphones, communications data, comparison with patient-reported outcome measures (PROMs), applications focusing on prediction of outcomes, monitoring, risk profiling, shared decision making, and surgical optimization. The rapidly evolving state of the art in digital phenotyping and capture of PGHD offers exciting prospects for outcome measurement in surgical care pending further work and consideration related to clinical care, technology, and implementation.
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Affiliation(s)
- Prakash Jayakumar
- Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA; (E.L.); (A.J.M.); (A.B.H.)
| | - Eugenia Lin
- Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA; (E.L.); (A.J.M.); (A.B.H.)
| | - Vincent Galea
- School of Medicine, New York Medical College, Valhalla, NY 10595, USA;
| | - Abraham J. Mathew
- Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA; (E.L.); (A.J.M.); (A.B.H.)
| | - Nikhil Panda
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Imelda Vetter
- Department of Medical Education, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Alex B. Haynes
- Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA; (E.L.); (A.J.M.); (A.B.H.)
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Kobsar D, Masood Z, Khan H, Khalil N, Kiwan MY, Ridd S, Tobis M. Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7143. [PMID: 33322187 PMCID: PMC7763184 DOI: 10.3390/s20247143] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/01/2020] [Accepted: 12/09/2020] [Indexed: 12/13/2022]
Abstract
Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for "Code Reuse" to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.
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Affiliation(s)
- Dylan Kobsar
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
| | - Zaryan Masood
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
| | - Heba Khan
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
| | - Noha Khalil
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
| | - Marium Yossri Kiwan
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
| | - Sarah Ridd
- Department of Psychology, Neuroscience, and Behaviour, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada;
| | - Matthew Tobis
- Department of Kinesiology, Faculty of Science, McMaster University, Hamilton, ON L8S 4L8, Canada; (Z.M.); (H.K.); (N.K.); (M.Y.K.); (M.T.)
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Dindorf C, Konradi J, Wolf C, Taetz B, Bleser G, Huthwelker J, Drees P, Fröhlich M, Betz U. General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of sex differences in spinal posture during stance and gait. Comput Methods Biomech Biomed Engin 2020; 24:299-307. [PMID: 33135504 DOI: 10.1080/10255842.2020.1828375] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Modern technologies enable to capture multiple biomechanical parameters often resulting in relational data. The current work proposes a generally applicable method comprising automated feature extraction, ensemble feature selection and classification to best capture the potentials of the data also for generating new biomechanical knowledge. Its benefits are demonstrated in the concrete biomechanically and medically relevant use case of gender classification based on spinal data for stance and gait. Very good results for accuracy were obtained using gait data. Dynamic movements of the lumbar spine in sagittal and frontal plane and of the pelvis in frontal plane best map gender differences.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Jürgen Konradi
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Claudia Wolf
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Bertram Taetz
- Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - Gabriele Bleser
- Junior Research Group wear HEALTH, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Janine Huthwelker
- Department of Orthopedics and Trauma Surgery, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Philipp Drees
- Department of Orthopedics and Trauma Surgery, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michael Fröhlich
- Department of Sports Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Ulrich Betz
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
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Dindorf C, Teufl W, Taetz B, Bleser G, Fröhlich M. Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4385. [PMID: 32781583 PMCID: PMC7471970 DOI: 10.3390/s20164385] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/04/2020] [Accepted: 08/04/2020] [Indexed: 01/31/2023]
Abstract
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model's accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, Germany;
| | - Wolfgang Teufl
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Bertram Taetz
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Gabriele Bleser
- Junior Research Group wearHEALTH, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany; (W.T.); (B.T.); (G.B.)
| | - Michael Fröhlich
- Department of Sports Science, Technische Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, 67663 Kaiserslautern, Germany;
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Relevance of sex, age and gait kinematics when predicting fall-risk and mortality in older adults. J Biomech 2020; 105:109723. [PMID: 32151381 DOI: 10.1016/j.jbiomech.2020.109723] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 11/22/2022]
Abstract
Approximately one-third of elderly people fall each year with severe consequences, including death. The aim of this study was to identify the most relevant features to be considered to maximize the accuracy of a logistic regression model designed for prediction of fall/mortality risk among older people. This study included 261 adults, aged over 65 years. Men and women were analyzed separately because sex stratification was revealed as being essential for our purposes of feature ranking and selection. Participants completed a 3-m walk test at their own gait velocity. An inertial sensor attached to their lumbar spine was used to record acceleration data in the three spatial directions. Signal processing techniques allowed the extraction of 21 features representative of gait kinematics, to be used as predictors to train and test the model. Age and gait speed data were also considered as predictors. A set of 23 features was considered. These features demonstrate to be more or less relevant depending on the sex of the cohort under analysis and the classification label (risk of falls and mortality). In each case, the minimum size subset of relevant features is provided to show the maximum accuracy prediction capability. Gait speed has been largely used as the single feature for the prediction fall risk among older adults. Nevertheless, prediction accuracy can be substantially improved, reaching 70% in some cases, if the task of training and testing the model takes into account some other features, namely, sex, age and gait kinematic parameters. Therefore we recommend considering sex, age and step regularity to predict fall-risk.
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