An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
Methods
Rotator cuff (RC) tears are common musculoskeletal injuries which often require surgical intervention. Noninvasive pulsed electromagnetic field (PEMF) devices have been approved for treatment of long-bone fracture nonunions and as an adjunct to lumbar and cervical spine fusion surgery. This study aimed to assess the effect of continuous PEMF on postoperative RC healing in a rat RC repair model. A total of 30 Wistar rats underwent acute bilateral supraspinatus tear and repair. A miniaturized electromagnetic device (MED) was implanted at the right shoulder and generated focused PEMF therapy. The animals’ left shoulders served as controls. Biomechanical, histological, and bone properties were assessed at three and six weeks.Aims
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