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

Development and Evaluation of Research Methods, Instruments and Tools (606) Tools, Programs and Algorithms (308) Machine Learning (1644) Emerging Technologies for Rehabilitation (292) Technology in Physiotherapy (147) Applications of AI (149)

Published on in Vol 9 (2025)

This is a member publication of JISC

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69150, first published .
Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study

Authors of this article:

Clement Twumasi1 Author Orcid Image ;   Mikail Aktas2 Author Orcid Image ;   Nicholas Santoni3 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics

Clement Twumasi   1 , BSc, MPhil, PhD ;   Mikail Aktas   2 * , MBBS, MRES ;   Nicholas Santoni   3 * , BSc

1 Nuffield Department of Medicine, Experimental Medicine Division, University of Oxford, Oxford, United Kingdom

2 Department of Bioengineering, Imperial College London, South Kensington, London, United Kingdom

3 Department of Biology, University of North Carolina, Greensboro, NC, United States

*these authors contributed equally

Corresponding Author:

  • Clement Twumasi, BSc, MPhil, PhD
  • Nuffield Department of Medicine
  • Experimental Medicine Division
  • University of Oxford
  • Peter Medawar Building for Pathogen Research
  • South Parks Road
  • Oxford, OX1 3SY
  • United Kingdom
  • Phone: 44 7411484454
  • Email: clement.twumasi@ndm.ox.ac.uk