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
Objectives. The aim of this study was to determine whether there is any significant
difference in temporal measurements of pain, function and rates
of re-tear for arthroscopic rotator cuff repair (RCR) patients compared
with those patients undergoing open RCR. Methods. This study compared questionnaire- and clinical examination-based
outcomes over two years or longer for two series of patients who
met the inclusion criteria: 200 open RCR and 200 arthroscopic RCR
patients. All surgery was performed by a single surgeon. . Results. Most pain measurements were similar for both groups. However, the
arthroscopic RCR group reported less night pain severity at six
months, less extreme pain and greater satisfaction with their overall
shoulder condition than the open RCR group. The arthroscopic RCR
patients also had earlier recovery of strength and
To evaluate if union of clavicle fractures can be predicted at six weeks post-injury by the presence of bridging callus on ultrasound. Adult patients managed nonoperatively with a displaced mid-shaft clavicle were recruited prospectively. Ultrasound evaluation of the fracture was undertaken to determine if sonographic bridging callus was present. Clinical risk factors at six weeks were used to stratify patients at high risk of nonunion with a combination of Quick Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) ≥ 40, fracture movement on examination, or absence of callus on radiograph.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
