Revealing Statistical Patterns in Shoulder Rehabilitation Exercises Characteristics

Tele-rehabilitation has the potential to transform the way patients are monitored from home, overcoming geographical barriers, enhancing accessibility, and promoting patient autonomy. The development of a tele-rehabilitation system capable of automating the recognition of performed exercises may significantly impact rehabilitation outcomes. Implementation of machine learning algorithms combined with magneto-inertial measurement units (M-IMUs) has enabled remote home-based rehabilitation therapy through wearable systems. Thus, in this study sixteen healthy participants and sixteen patients with rotator cuff injuries were enrolled to perform six shoulder rehabilitation exercises while wearing a wearable system based on three M-IMUs. This study aimed to conduct a thorough analysis of the features extracted from time-series data collected by these three sensors during these exercises. The statistical analysis indicated statistically significant differences in task features, but not between participant groups. Three features, identified as the most representative and distinctive among all tasks, were subsequently, used to train the Support Vector Classifier in classifying the six exercises. The obtained classification results are promising for the application of this wearable device in remote monitoring of patients with shoulder musculoskeletal disorders during home-based rehabilitation exercises. Further studies will involve the implementation of the Principal Component Analysis (PCA), along with the training of additional machine learning models.