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

Rotator cuff (RC) injuries are characterized by tendon rupture, muscle atrophy, retraction, and fatty infiltration, which increase injury severity and jeopardize adequate tendon repair. Epigenetic drugs, such as histone deacetylase inhibitors (HDACis), possess the capacity to redefine the molecular signature of cells, and they may have the potential to inhibit the transformation of the fibro-adipogenic progenitors (FAPs) within the skeletal muscle into adipocyte-like cells, concurrently enhancing the myogenic potential of the satellite cells.

Aims

Rotator cuff tear (RCT) is the leading cause of shoulder pain, primarily associated with age-related tendon degeneration. This study aimed to elucidate the potential differential gene expressions in tendons across different age groups, and to investigate their roles in tendon degeneration.

Objectives

Indocyanine green (ICG) fluorescence angiography is an emerging technique that can provide detailed anatomical information during surgery. The purpose of this study is to determine whether ICG fluorescence angiography can be used to evaluate the blood flow of the rotator cuff tendon in the clinical setting.

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.