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For: Juen J, Cheng Q, Prieto-Centurion V, Krishnan JA, Schatz B. Health monitors for chronic disease by gait analysis with mobile phones. Telemed J E Health 2014;20:1035-41. [PMID: 24694291 DOI: 10.1089/tmj.2014.0025] [Cited by in Crossref: 36] [Cited by in F6Publishing: 18] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
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6 Panchal UK, Ajmani H, Sait SY. Flooding Level Classification by Gait Analysis of Smartphone Sensor Data. IEEE Access 2019;7:181678-87. [DOI: 10.1109/access.2019.2959557] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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8 Siasios ID, Spanos SL, Kanellopoulos AK, Fotiadou A, Pollina J, Schneider D, Becker A, Dimopoulos VG, Fountas KN. The Role of Gait Analysis in the Evaluation of Patients with Cervical Myelopathy: A Literature Review Study. World Neurosurg 2017;101:275-82. [PMID: 28192261 DOI: 10.1016/j.wneu.2017.01.122] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
9 Holland AE, Malaguti C, Hoffman M, Lahham A, Burge AT, Dowman L, May AK, Bondarenko J, Graco M, Tikellis G, Lee JY, Cox NS. Home-based or remote exercise testing in chronic respiratory disease, during the COVID-19 pandemic and beyond: A rapid review. Chron Respir Dis 2020;17:1479973120952418. [PMID: 32840385 DOI: 10.1177/1479973120952418] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
10 Shah VV, Curtze C, Sowalsky K, Arpan I, Mancini M, Carlson-kuhta P, El-gohary M, Horak FB, Mcnames J. Inertial Sensor Algorithm to Estimate Walk Distance. Sensors 2022;22:1077. [DOI: 10.3390/s22031077] [Reference Citation Analysis]
11 Lemoyne R, Mastroianni T. IMPLEMENTATION OF A SMARTPHONE AS A WIRELESS ACCELEROMETER PLATFORM FOR QUANTIFYING HEMIPLEGIC GAIT DISPARITY IN A FUNCTIONALLY AUTONOMOUS CONTEXT. J Mech Med Biol 2018;18:1850005. [DOI: 10.1142/s0219519418500057] [Cited by in Crossref: 6] [Article Influence: 1.5] [Reference Citation Analysis]
12 Al-Yahya E, Mohammad MT, Muhaidat J, Demour SA, Qutishat D, Al-Khlaifat L, Okasheh R, Lawrie S, Esser P, Dawes H. Functional Balance and Gait Characteristics in Men With Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia. Am J Mens Health 2019;13:1557988319839879. [PMID: 31081440 DOI: 10.1177/1557988319839879] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
13 Alafeef M, Fraiwan M. On the diagnosis of idiopathic Parkinson’s disease using continuous wavelet transform complex plot. J Ambient Intell Human Comput 2019;10:2805-15. [DOI: 10.1007/s12652-018-1014-x] [Cited by in Crossref: 12] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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15 Sejdić E, Millecamps A, Teoli J, Rothfuss MA, Franconi NG, Perera S, Jones AK, Brach JS, Mickle MH. Assessing interactions among multiple physiological systems during walking outside a laboratory: An Android based gait monitor. Comput Methods Programs Biomed 2015;122:450-61. [PMID: 26390946 DOI: 10.1016/j.cmpb.2015.08.012] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 1.6] [Reference Citation Analysis]
16 Ebara T, Azuma R, Shoji N, Matsukawa T, Yamada Y, Akiyama T, Kurihara T, Yamada S. Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels. J Occup Health 2017;59:506-12. [PMID: 28835575 DOI: 10.1539/joh.17-0101-OA] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 1.4] [Reference Citation Analysis]
17 Storm FA, Cesareo A, Reni G, Biffi E. Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review. Sensors (Basel) 2020;20:E2660. [PMID: 32384806 DOI: 10.3390/s20092660] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
18 Xu X, Nemati E, Vatanparvar K, Nathan V, Ahmed T, Rahman MM, Mccaffrey D, Kuang J, Gao JA. Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio. Proc ACM Interact Mob Wearable Ubiquitous Technol 2021;5:1-22. [DOI: 10.1145/3448124] [Cited by in Crossref: 8] [Cited by in F6Publishing: 1] [Article Influence: 8.0] [Reference Citation Analysis]
19 Lee H, Lee S, Salado L, Estrada J, White J, Muthukumar V, Lee SP, Mohapatra S. Proof-of-Concept Testing of a Real-Time mHealth Measure to Estimate Postural Control During Walking: A Potential Application for Mild Traumatic Brain Injuries. Asian Pac Isl Nurs J 2018;3:177-89. [PMID: 31037266 DOI: 10.31372/20180304.1027] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Hua A, Quicksall Z, Di C, Motl R, LaCroix AZ, Schatz B, Buchner DM. Accelerometer-based predictive models of fall risk in older women: a pilot study. NPJ Digit Med 2018;1:25. [PMID: 31304307 DOI: 10.1038/s41746-018-0033-5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 13] [Article Influence: 5.3] [Reference Citation Analysis]
21 Keat RM, Thomas M, McKechnie A. Ten thousand steps: a pedometer study of junior dentists in a major British teaching hospital and a district general hospital. Br J Oral Maxillofac Surg 2017;55:e12-6. [PMID: 27955929 DOI: 10.1016/j.bjoms.2016.11.322] [Cited by in Crossref: 1] [Article Influence: 0.2] [Reference Citation Analysis]
22 Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019;7:e12649. [PMID: 31444874 DOI: 10.2196/12649] [Cited by in Crossref: 26] [Cited by in F6Publishing: 16] [Article Influence: 8.7] [Reference Citation Analysis]
23 Adhikary S, Ghosh A. Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing. Biomedical Signal Processing and Control 2022;72:103321. [DOI: 10.1016/j.bspc.2021.103321] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Coultas DB, Jackson BE, Russo R, Peoples J, Singh KP, Sloan J, Uhm M, Ashmore JA, Blair SN, Bae S. Home-based Physical Activity Coaching, Physical Activity, and Health Care Utilization in Chronic Obstructive Pulmonary Disease. Chronic Obstructive Pulmonary Disease Self-Management Activation Research Trial Secondary Outcomes. Ann Am Thorac Soc 2018;15:470-8. [PMID: 29283670 DOI: 10.1513/AnnalsATS.201704-308OC] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 4.7] [Reference Citation Analysis]
25 Schatz BR. National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors. Big Data 2015;3:219-29. [PMID: 26858915 DOI: 10.1089/big.2015.0021] [Cited by in Crossref: 20] [Cited by in F6Publishing: 10] [Article Influence: 2.9] [Reference Citation Analysis]
26 Cheng Q, Juen J, Bellam S, Fulara N, Close D, Silverstein JC, Schatz B. Predicting Pulmonary Function from Phone Sensors. Telemed J E Health 2017;23:913-9. [PMID: 28300524 DOI: 10.1089/tmj.2017.0008] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.6] [Reference Citation Analysis]
27 Corman BHP, Rajupet S, Ye F, Schoenfeld ER. The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Post-Acute Sequelae of SARS CoV-2. J Med Internet Res 2021. [PMID: 34932496 DOI: 10.2196/32713] [Reference Citation Analysis]
28 Capela NA, Lemaire ED, Baddour N. Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation. J Neuroeng Rehabil 2015;12:19. [PMID: 25889112 DOI: 10.1186/s12984-015-0013-9] [Cited by in Crossref: 44] [Cited by in F6Publishing: 25] [Article Influence: 6.3] [Reference Citation Analysis]
29 Liu X, Zhao C, Zheng B, Guo Q, Duan X, Wulamu A, Zhang D. Wearable Devices for Gait Analysis in Intelligent Healthcare. Front Comput Sci 2021;3:661676. [DOI: 10.3389/fcomp.2021.661676] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Jourdan T, Debs N, Frindel C. The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. Sensors (Basel) 2021;21:4808. [PMID: 34300546 DOI: 10.3390/s21144808] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
31 Zou Q, Wang Y, Wang Q, Zhao Y, Li Q. Deep Learning-Based Gait Recognition Using Smartphones in the Wild. IEEE Trans Inform Forensic Secur 2020;15:3197-212. [DOI: 10.1109/tifs.2020.2985628] [Cited by in Crossref: 50] [Cited by in F6Publishing: 11] [Article Influence: 25.0] [Reference Citation Analysis]
32 Cheng Q, Juen J, Hsu-Lumetta J, Schatz B. Predicting Transitions in Oxygen Saturation Using Phone Sensors. Telemed J E Health 2016;22:132-7. [PMID: 30175953 DOI: 10.1089/tmj.2015.0040] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
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