Aims. 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. Methods. 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. Results. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion. Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

Aims

This study aimed to define the histopathology of degenerated humeral head cartilage and synovial inflammation of the glenohumeral joint in patients with omarthrosis (OmA) and cuff tear arthropathy (CTA). Additionally, the potential of immunohistochemical tissue biomarkers in reflecting the degeneration status of humeral head cartilage was evaluated.

Aims

To evaluate if union of clavicle fractures can be predicted at six weeks post-injury by the presence of bridging callus on ultrasound.

Objectives

The objective of this study was to perform a meta-analysis of all randomised controlled trials (RCTs) comparing surgical and non-surgical management of fractures of the proximal humerus, and to determine whether further analyses based on complexity of fracture, or the type of surgical intervention, produced disparate findings on patient outcomes.

Aims

We performed a systematic review of the literature to determine whether earlier surgical repair of acute rotator cuff tear (ARCT) leads to superior post-operative clinical outcomes.

Objectives

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.