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Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider

Clement Twumasi¹, Mikail Aktas², Nicholas Santoni³

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

JMIR Formative Research

|March 18, 2025

Summary

This summary is machine-generated.

This study used clustering analysis on home-based knee movement data to identify distinct patterns. Findings reveal key predictors like BMI and gender, paving the way for personalized rehabilitation strategies.

Keywords:

Slider device cluster analysis digital health force measurement knee osteoarthritis knee replacement machine learning musculoskeletal physical therapy telerehabilitation

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Area of Science:

Rehabilitation Sciences
Biomechanical Data Analysis
Computational Techniques

Background:

Computational techniques enhance rehabilitation diagnostics and treatment.
High-dimensional, time-dependent data analysis remains a challenge.
Biomechanical data analysis shows potential for clinical decision-making.

Purpose of the Study:

Analyze multidimensional movement datasets from a novel home exercise device.
Identify clinically relevant movement patterns for personalized rehabilitation.
Predict recovery trajectories and assess postoperative complication risks.

Main Methods:

Applied four unsupervised clustering techniques (k-means, hierarchical, PAM, CLARA) to knee kinetic data from 32 participants.
Utilized force, laser distance, and optical tracker data from lower limb activities.
Evaluated cluster performance using silhouette analysis and identified key demographic and pain predictors via logistic regression.

Main Results:

Identified three distinct, time-varying movement patterns for each knee.
Hierarchical clustering excelled for the right knee (silhouette 0.637), CLARA for the left (silhouette 0.598).
BMI significantly influenced right knee cluster membership; gender was a key predictor for the left knee.

Conclusions:

Identified kinetic patterns offer insights for personalized rehabilitation protocols.
Demonstrated the effectiveness of unsupervised clustering in biomechanical data analysis for rehabilitation.
Highlights the potential for improved patient outcomes through data-driven clinical decisions.