# Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch

arXiv: https://arxiv.org/abs/1802.01489 · HTML: https://mlsanity.com/paper/1802.01489/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessment](https://mlsanity.com/paper/2312.13509/) (2023)
- [MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy](https://mlsanity.com/paper/2408.11837/) (2024)
- [Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos](https://mlsanity.com/paper/2106.13967/) (2021)
- [My(o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack](https://mlsanity.com/paper/2112.02382/) (2021)
- [A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises](https://mlsanity.com/paper/2606.30309/) (2026)

## Abstract (arXiv)

Objective: Participation in a physical therapy program is considered one of the greatest predictors of successful conservative management of common shoulder disorders. However, adherence to these protocols is often poor and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch. Approach: Twenty healthy adult subjects with no prior shoulder disorders performed seven exercises from an evidence-based rotator cuff physiotherapy protocol, while 6-axis inertial sensor data was collected from the active extremity. Within an activity recognition chain (ARC) framework, four supervised learning algorithms were trained and optimized to classify the exercises: k-nearest neighbor (k-NN), random forest (RF), support vector machine classifier (SVC), and a convolutional recurrent neural network (CRNN). Algorithm performance was evaluated using 5-fold cross-validation stratified first temporally and then by subject. Main Results: Categorical classification accuracy was above 94% for all algorithms on the temporally stratified cross validation, with the best performance achieved by the CRNN algorithm (99.4%). The subject stratified cross validation, which evaluated classifier performance on unseen subjects, yielded lower accuracies scores again with CRNN performing best (88.9%). Significance: This proof of concept study demonstrates the technical feasibility of a smartwatch device and supervised machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols.