Evaluating Time Series Representation Methods for Rehabilitation Exercise Recognition

Wearable sensors (e.g., accelerometers, gyroscopes) combined with machine learning (ML) methods offer a promising solution for remote monitoring of physical rehabilitation exercises. However, challenges arise given the high variability in how patients perform the same exercises, combined with the scarcity of labeled data. This limits the effectiveness of standard supervised learning approaches. This paper analyzes and quantifies key aspects of developing generalizable physical exercise recognition models through the lense of ML training procedures. We make four contributions: (i) evaluating time-domain data augmentations on model performance, (ii) quantifying interperson variability using within-dataset incremental fine-tuning, (iii) assessing transferability across datasets with encoders pretrained in a supervised manner, and (iv) comparing supervised and unsupervised pretraining strategies using free-living and exercise-specific motion data.