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

Development and Evaluation of Research Methods, Instruments and Tools (1310) Tools, Programs and Algorithms (581) Emerging Technologies for Rehabilitation (502) Technology in Physiotherapy (290) Machine Learning (3128) Applications of AI (928)

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 .
Foot on blue slider device for foot exercises

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 (4) Tweetations (1) Metrics Author Video

Journals

  1. Gupta D, Gupta D, Bashir U, Nnap L, Rawat A, Dolphin J, Sultana R, Cai L, Imam B, Devkota R, Dsouza D, Rai M. Artificial Intelligence in Rheumatology: Clinical Applications in Rheumatoid Arthritis, Osteoarthritis, and Systemic Lupus Erythematosus. Cureus 2025 View
  2. Ghasemi Rad Abiyaneh S, Hashemi R, Archer C, Ghadirinejad K. Challenges and Limitations of Machine Learning in Total Joint Arthroplasty: Insights from Recent Studies. Prosthesis 2026;8(4):35 View
  3. Martinez-Zujeros S, Zufiria P, Sasot A, Delgado-Losada M. Robust Unsupervised Analysis of Longitudinal Functional Trajectories in Older Inpatients Reveals Stable Recovery Profiles: The AIRCOT Study. European Journal of Investigation in Health, Psychology and Education 2026;16(8):111 View

Books/Policy Documents

  1. PANDA S, BALI S. AI‐driven Innovations in Physiotherapy and Oncology 2. View