Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.

ABSTRACT:

Purpose

The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises.

Methods

The cohort included both healthy and patients with rotator-cuff (RC) tears. All participants performed six shoulder rehabilitation exercises, following guidelines developed by the American Society of Shoulder and Elbow Therapists. Each exercise was repeated six times, while wearing a wearable system equipped with three magneto-inertial sensors. Six supervised machine-learning models (k-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest (RF), Logistic Regression and Adaptive Boosting) were trained for the classification. The algorithms' ability to accurately classify exercise activities was evaluated u

SUBMITTER: Sassi M 

PROVIDER: S-EPMC11948177 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature