Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study
These kinetic patterns offer significant insights for creating personalized rehabilitation procedures, potentially improving patient outcomes. These findings underscore the efficacy of unsupervised clustering techniques in the analysis of biomechanical data for clinical rehabilitation applications.
Authors
- Mikail Aktas (ORCID: https://orcid.org/0000-0002-7712-1367)
- Clement Twumasi (ORCID: https://orcid.org/0000-0001-6817-8356)
- N.B. Santoni
Institutions
- University of North Carolina at Greensboro (US)
- University of Oxford (GB)
- Imperial College London (GB)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-03-18
- DOI
- https://doi.org/10.2196/69150
- Citations
- 2
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.01