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
Traumatic brachial plexus injury causes severe functional impairment
of the arm. Elbow flexion is often affected. Nerve surgery or tendon
transfers provide the only means to obtain improved elbow flexion.
Unfortunately, the functionality of the arm often remains insufficient.
Stem cell therapy could potentially improve muscle strength and
avoid muscle-tendon transfer. This pilot study assesses the safety
and regenerative potential of autologous bone marrow-derived mononuclear
cell injection in partially denervated biceps. Nine brachial plexus patients with insufficient elbow flexion
(i.e., partial denervation) received intramuscular escalating doses
of autologous bone marrow-derived mononuclear cells, combined with
tendon transfers. Effect parameters included biceps biopsies, motor
unit analysis on needle electromyography and computerised muscle tomography,
before and after cell therapy.Objectives
Methods
