Smart Physiotherapy Activity Recognition System (SPARS): Shifting the paradigm in home physiotherapy

Physical therapy is essential for the successful rehabilitation of common shoulder injuries and following shoulder surgery. Patients may receive some training and supervision for shoulder physiotherapy through private pay or private insurance, but they are typically responsible for performing most of their physiotherapy independently at home. It is unknown to what degree patients perform their home exercises and if the exercises are done correctly without supervision. Our team has recently developed a Smart Physiotherapy Activity Recognition System (SPARS) for tracking home shoulder physiotherapy exercises using sensors in a commercial smart watch and artificial intelligence (AI). SPARS has been successfully shown to track shoulder exercises in healthy adults in the laboratory setting. Further inquiry is required to establish the clinical effectiveness of this technology and investigate the potential individual and societal impacts of its use. A clinical study focused on both implementation and implications of adherence monitoring with AI in patients with rotator cuff pathology is planned to be carried out within the Working Conditions Program at the Holland Centre.

Open source code available

Seglearn is a python package for machine learning time series or sequences which was developed for this project. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator. Seglearn provides a flexible approach to multivariate time series and related contextual (meta) data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models. It is compatible with scikit-learn.