Kinetic pattern recognition in home-based knee rehabilitation using machine learning clustering methods on the slider digital physiotherapy device: prospective observational study

Journal article

Abstract:: Background: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant problem. Prior research has used clustering techniques on rehabilitation data to identify movement patterns and forecast recovery outcomes. Nonetheless, these initiatives have not yet used force or motion datasets obtained out...
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Files:: Twumasi_et_al_2025_Kinetic_pattern_recognition.pdf

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Language:: English
Keywords:: machine learning

cluster analysis

force measurement

knee replacement

musculoskeletal

physical therapy

Slider device

knee osteoarthritis

digital health

telerehabilitation
Pubs id:: 2095189
Local pid:: pubs:2095189
Deposit date:: 2025-03-18
ARK identifier:: ark:/29072/ora_b99f120827864ee0803fbd0e3878396d

Copyright holder:: Twumasi et al.
Rights statement:: ©Clement Twumasi, Mikail Aktas, Nicholas Santoni. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.03.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

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