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

Emerging Technologies for Rehabilitation (256) Human Factors and Usability Case Studies (772) Telerehabilitation (154) mHealth for Rehabilitation (257) Assistive Technologies (112)

Published on in Vol 10 (2023)

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45993, first published .
Examining Usability, Acceptability, and Adoption of a Self-Directed, Technology-Based Intervention for Upper Limb Rehabilitation After Stroke: Cohort Study

Examining Usability, Acceptability, and Adoption of a Self-Directed, Technology-Based Intervention for Upper Limb Rehabilitation After Stroke: Cohort Study

Examining Usability, Acceptability, and Adoption of a Self-Directed, Technology-Based Intervention for Upper Limb Rehabilitation After Stroke: Cohort Study

Authors of this article:

Michelle Broderick1 Author Orcid Image ;   Robert O'Shea2 Author Orcid Image ;   Jane Burridge3 Author Orcid Image ;   Sara Demain3 Author Orcid Image ;   Louise Johnson3, 4 Author Orcid Image ;   Paul Bentley1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Jeter R, Greenfield R, Housley S, Belykh I. Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach. JMIR Biomedical Engineering 2024;9:e56980 View