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For: Hobbs B, Artemiadis P. A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation. Front Neurorobot 2020;14:19. [PMID: 32351377 DOI: 10.3389/fnbot.2020.00019] [Cited by in Crossref: 17] [Cited by in F6Publishing: 10] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Varas-diaz G, Bhatt T, Oken B, Roth E, Hayes J, Cordo P. Concurrent ankle-assisted movement, biofeedback, and proprioceptive stimulation reduces lower limb motor impairment and improves gait in persons with stroke. Physiotherapy Theory and Practice. [DOI: 10.1080/09593985.2022.2122763] [Reference Citation Analysis]
2 Gao J, Ma C, Wu D, Xu X, Wang S, Yao J, Hu S. Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm. Journal of Sensors 2022;2022:1-15. [DOI: 10.1155/2022/3015645] [Reference Citation Analysis]
3 Aprile I, Conte C, Cruciani A, Pecchioli C, Castelli L, Insalaco S, Germanotta M, Iacovelli C. Efficacy of Robot-Assisted Gait Training Combined with Robotic Balance Training in Subacute Stroke Patients: A Randomized Clinical Trial. JCM 2022;11:5162. [DOI: 10.3390/jcm11175162] [Reference Citation Analysis]
4 Guo Y, Yang J, Liu Y, Chen X, Yang G. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022;14:916971. [DOI: 10.3389/fnagi.2022.916971] [Reference Citation Analysis]
5 Zuccon G, Lenzo B, Bottin M, Rosati G. Rehabilitation robotics after stroke: a bibliometric literature review. Expert Rev Med Devices 2022. [PMID: 35786139 DOI: 10.1080/17434440.2022.2096438] [Reference Citation Analysis]
6 Lee L, Li I, Lu L, Hsu Y, Chiou S, Su T. Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot. Applied Sciences 2022;12:5979. [DOI: 10.3390/app12125979] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Gil-castillo J, Barria P, Aguilar Cárdenas R, Baleta Abarza K, Andrade Gallardo A, Biskupovic Mancilla A, Azorín JM, Moreno JC. A Robot-Assisted Therapy to Increase Muscle Strength in Hemiplegic Gait Rehabilitation. Front Neurorobot 2022;16:837494. [DOI: 10.3389/fnbot.2022.837494] [Reference Citation Analysis]
8 Baysal CV. An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles. Actuators 2022;11:111. [DOI: 10.3390/act11040111] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Ballen-Moreno F, Bautista M, Provot T, Bourgain M, Cifuentes CA, Múnera M. Development of a 3D Relative Motion Method for Human-Robot Interaction Assessment. Sensors (Basel) 2022;22:2411. [PMID: 35336593 DOI: 10.3390/s22062411] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Selamat SNS, Che Me R, Ahmad Ainuddin H, Salim MSF, Ramli HR, Romli MH. The Application of Technological Intervention for Stroke Rehabilitation in Southeast Asia: A Scoping Review With Stakeholders' Consultation. Front Public Health 2022;9:783565. [DOI: 10.3389/fpubh.2021.783565] [Reference Citation Analysis]
11 Monoscalco L, Simeoni R, Maccioni G, Giansanti D. Information Security in Medical Robotics: A Survey on the Level of Training, Awareness and Use of the Physiotherapist. Healthcare 2022;10:159. [DOI: 10.3390/healthcare10010159] [Reference Citation Analysis]
12 Wang Y, Li Z, Wang X, Yu H, Liao W, Arifoglu D. Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism. Machines 2021;9:367. [DOI: 10.3390/machines9120367] [Reference Citation Analysis]
13 Rose L, Bazzocchi MCF, Nejat G. A model-free deep reinforcement learning approach for control of exoskeleton gait patterns. Robotica. [DOI: 10.1017/s0263574721001600] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
14 Robinson N, Mane R, Chouhan T, Guan C. Emerging trends in BCI-robotics for motor control and rehabilitation. Current Opinion in Biomedical Engineering 2021;20:100354. [DOI: 10.1016/j.cobme.2021.100354] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Zhang Y, Cao G, Ling Z, Li W, Cheng H, He B, Cao S, Zhu A. A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton. Front Neurorobot 2021;15:692539. [PMID: 34795571 DOI: 10.3389/fnbot.2021.692539] [Reference Citation Analysis]
16 Lo WA, Chen D, Zhao J, Leng Y, Bian R, Huang W, Liang Y, Mao YR, Huang DF. The Efficacy of Interlimb-Coordinated Intervention on Gait and Motor Function Recovery in Patients with Acute Stroke: A Multi-Center Randomized Controlled Trial Study Protocol. Brain Sci 2021;11:1495. [PMID: 34827494 DOI: 10.3390/brainsci11111495] [Reference Citation Analysis]
17 Zhang Y, Cao G, Li W, Chen J, Li L, Diao D. A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot. Applied Sciences 2021;11:10329. [DOI: 10.3390/app112110329] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Wang X, Liu G, Feng Y, Li W, Niu J, Gan Z. Measurement Method of Human Lower Limb Joint Range of Motion Through Human-Machine Interaction Based on Machine Vision. Front Neurorobot 2021;15:753924. [PMID: 34720913 DOI: 10.3389/fnbot.2021.753924] [Reference Citation Analysis]
19 Ishibashi K, Yoshikawa K, Koseki K, Aoyama T, Ishii D, Yamamoto S, Matsuda T, Tomita K, Mutsuzaki H, Kohno Y. Gait Training after Stroke with a Wearable Robotic Device: A Case Report of Further Improvements in Walking Ability after a Recovery Plateau. Prog Rehabil Med 2021;6:20210037. [PMID: 34595360 DOI: 10.2490/prm.20210037] [Reference Citation Analysis]
20 Yamamoto H, Takeda K, Koyama S, Morishima K, Hirakawa Y, Motoya I, Sakurai H, Kanada Y, Kawamura N, Kawamura M, Tanabe S. The relationship between upper limb function and activities of daily living without the effects of lower limb function: A cross-sectional study. British Journal of Occupational Therapy. [DOI: 10.1177/03080226211030088] [Reference Citation Analysis]
21 Porciuncula F, Baker TC, Arumukhom Revi D, Bae J, Sloutsky R, Ellis TD, Walsh CJ, Awad LN. Targeting Paretic Propulsion and Walking Speed With a Soft Robotic Exosuit: A Consideration-of-Concept Trial. Front Neurorobot 2021;15:689577. [PMID: 34393750 DOI: 10.3389/fnbot.2021.689577] [Reference Citation Analysis]
22 Calabrò RS, Sorrentino G, Cassio A, Mazzoli D, Andrenelli E, Bizzarini E, Campanini I, Carmignano SM, Cerulli S, Chisari C, Colombo V, Dalise S, Fundarò C, Gazzotti V, Mazzoleni D, Mazzucchelli M, Melegari C, Merlo A, Stampacchia G, Boldrini P, Mazzoleni S, Posteraro F, Benanti P, Castelli E, Draicchio F, Falabella V, Galeri S, Gimigliano F, Grigioni M, Mazzon S, Molteni F, Morone G, Petrarca M, Picelli A, Senatore M, Turchetti G, Bonaiuti D; Italian Consensus Conference on Robotics in Neurorehabilitation (CICERONE). Robotic-assisted gait rehabilitation following stroke: a systematic review of current guidelines and practical clinical recommendations. Eur J Phys Rehabil Med 2021;57:460-71. [PMID: 33947828 DOI: 10.23736/S1973-9087.21.06887-8] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
23 Nizamis K, Athanasiou A, Almpani S, Dimitrousis C, Astaras A. Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges. Sensors (Basel) 2021;21:2084. [PMID: 33809721 DOI: 10.3390/s21062084] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Maggio MG, Naro A, Manuli A, Maresca G, Balletta T, Latella D, De Luca R, Calabrò RS. Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach. Brain Topogr 2021;34:348-62. [PMID: 33661430 DOI: 10.1007/s10548-021-00825-5] [Reference Citation Analysis]
25 Khan H, Naseer N, Yazidi A, Eide PK, Hassan HW, Mirtaheri P. Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review. Front Hum Neurosci 2020;14:613254. [PMID: 33568979 DOI: 10.3389/fnhum.2020.613254] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Kyrarini M, Lygerakis F, Rajavenkatanarayanan A, Sevastopoulos C, Nambiappan HR, Chaitanya KK, Babu AR, Mathew J, Makedon F. A Survey of Robots in Healthcare. Technologies 2021;9:8. [DOI: 10.3390/technologies9010008] [Cited by in Crossref: 17] [Cited by in F6Publishing: 1] [Article Influence: 17.0] [Reference Citation Analysis]
27 Kim HK, Seong S, Park J, Kim J, Park J, Park W. Subjective Evaluation of the Effect of Exoskeleton Robots for Rehabilitation Training. IEEE Access 2021;9:130554-61. [DOI: 10.1109/access.2021.3112263] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Lora-millan JS, Hidalgo AF, Rocon E. An IMUs-Based Extended Kalman Filter to Estimate Gait Lower Limb Sagittal Kinematics for the Control of Wearable Robotic Devices. IEEE Access 2021;9:144540-54. [DOI: 10.1109/access.2021.3122160] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Giansanti D. The Rehabilitation and the Robotics: Are They Going Together Well? Healthcare (Basel) 2020;9:26. [PMID: 33396636 DOI: 10.3390/healthcare9010026] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
30 Kotov SV, Isakova EV, Lijdvoy VY, Petrushanskaya KA, Pismennaya EV, Romanova MV, Kodzokova LH. [Robotic recovery of walking function in patients in the early recovery period of stroke]. Zh Nevrol Psikhiatr Im S S Korsakova 2020;120:73-80. [PMID: 33016680 DOI: 10.17116/jnevro202012008273] [Reference Citation Analysis]
31 Nascimento LMSD, Bonfati LV, Freitas MB, Mendes Junior JJA, Siqueira HV, Stevan SL Jr. Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review. Sensors (Basel) 2020;20:E4063. [PMID: 32707749 DOI: 10.3390/s20154063] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
32 Yin G, Zhang X, Chen D, Li H, Chen J, Chen C, Lemos S. Processing Surface EMG Signals for Exoskeleton Motion Control. Front Neurorobot 2020;14:40. [PMID: 32765250 DOI: 10.3389/fnbot.2020.00040] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]