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
Injectable Bromelain Solution (IBS) is a modified investigational derivate of the medical grade bromelain-debriding pharmaceutical agent (NexoBrid) studied and approved for a rapid (four-hour single application), eschar-specific, deep burn debridement. We conducted an Specially prepared medical grade IBS was injected into fresh Dupuytren’s cords excised from patients undergoing surgical fasciectomy. These cords were tested by tension-loading them to failure with the Zwick 1445 (Zwick GmbH & Co. KG, Ulm, Germany) tension testing system.Objectives
Materials and Methods
