  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.
