This paper is in the following e-collection/theme issue:

Digital Health Reviews (1289) Stroke (163) Technology in Physiotherapy (134) Portable and Mobile Technologies for Rehabilitation (151)

Published on in Vol 24, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34307, first published .
Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Authors of this article:

Xiaoyi Wang1 Author Orcid Image ;   Yan Fu1 Author Orcid Image ;   Bing Ye2, 3 Author Orcid Image ;   Jessica Babineau4 Author Orcid Image ;   Yong Ding5 Author Orcid Image ;   Alex Mihailidis2, 3 Author Orcid Image
Article Authors Cited by (11) Tweetations (4) Metrics

Journals

  1. Judy L, Morrow C, Seo N. Development and evaluation of an efficient training program to facilitate the adoption of a novel neurorehabilitation device. Journal of Rehabilitation and Assistive Technologies Engineering 2023;10 View
  2. Zhao Z, Wang J, Wang S, Wang R, Lu Y, Yuan Y, Chen J, Dai Y, Liu Y, Wang X, Pan Y, Gao S. Multimodal Sensing in Stroke Motor Rehabilitation. Advanced Sensor Research 2023;2(9) View
  3. Bandini V, Carpinella I, Marzegan A, Jonsdottir J, Frigo C, Avanzino L, Pelosin E, Ferrarin M, Lencioni T. Surface-Electromyography-Based Co-Contraction Index for Monitoring Upper Limb Improvements in Post-Stroke Rehabilitation: A Pilot Randomized Controlled Trial Secondary Analysis. Sensors 2023;23(17):7320 View
  4. Cheng H, Chin L, Kanzler C, Lehner R, Kuah C, Kager S, Josse E, Samkharadze T, Sidarta A, Gonzalez P, Lie E, Zbytniewska-Mégret M, Wee S, Liang P, Gassert R, Chua K, Lambercy O, Wenderoth N. Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform. Frontiers in Neurology 2023;14 View
  5. Ito D, Fukuda M, Hosoi Y, Hirose R, Teramae T, Kamimoto T, Yamada Y, Tsuji T, Noda T, Kawakami M. Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study. BMC Neurology 2024;24(1) View
  6. Fu Y, Zhang Y, Ye B, Babineau J, Zhao Y, Gao Z, Mihailidis A. Smartphone-Based Hand Function Assessment: Systematic Review. Journal of Medical Internet Research 2024;26:e51564 View
  7. Ding K, Wang J, Wang X, Zhou L, Xiong D, Guo L. System for Detection and Quantitative Evaluation of Compensatory Movement in Post-Stroke Patients Based on Wearable Sensor and Machine Learning Algorithm. IEEE Sensors Journal 2024;24(14):22830 View
  8. Xuan Chua M, Okubo Y, Peng S, Nho Do T, Hui Wang C, Wu L. Analysis of Fatigue-Induced Compensatory Movements in Bicep Curls: Gaining Insights for the Deployment of Wearable Sensors. IEEE Transactions on Medical Robotics and Bionics 2024;6(3):1147 View
  9. Cocco E, Pournajaf S, Romano P, Morone G, Thouant C, Buscarini L, Manzia C, Cioeta M, Felzani G, Infarinato F, Franceschini M, Goffredo M. Comparative analysis of upper body kinematics in stroke, Parkinson's disease, and healthy subjects: An observational study using IMU-based targeted box and block test. Gait & Posture 2024;114:69 View

Books/Policy Documents

  1. Tello E, Yanadel A, Perez M, López N. Advances in Bioengineering and Clinical Engineering. View
  2. Srinivasan S, Rajavel N. AI Technologies and Advancements for Psychological Well-Being and Healthcare. View