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
Cadaveric models of the shoulder evaluate discrete motion segments
using the glenohumeral joint in isolation over a defined trajectory.
The aim of this study was to design, manufacture and validate a
robotic system to accurately create three-dimensional movement of
the upper body and capture it using high-speed motion cameras. In particular, we intended to use the robotic system to simulate
the normal throwing motion in an intact cadaver. The robotic system
consists of a lower frame (to move the torso) and an upper frame
(to move an arm) using seven actuators. The actuators accurately
reproduced planned trajectories. The marker setup used for motion
capture was able to determine the six degrees of freedom of all
involved joints during the planned motion of the end effector.Objectives
Methods
