Artificial Intelligence and Wearable Sensor-Based Prediction of Upper-Limb Overuse Injuries in Wheelchair Para Athletes: Implications for Sports Physiotherapy

Abstract

Upper-limb overuse pathology, particularly of the shoulder and wrist, is the leading cause of pain and impaired performance among wheelchair para athletes, arising from the paradox of using load-bearing joints originally suited for mobility rather than sustained propulsive force. Traditional clinical screening relies on intermittent, subjective assessment and laboratory-based biomechanical testing, both of which are poorly suited to capturing the cumulative, ecologically valid loading histories that precipitate overuse injury. Recent convergence of low-cost inertial measurement units (IMU), surface electromyography (EMG), and pushrim force sensors with machine learning (ML) and deep learning (DL) analytics has opened a pathway toward continuous, field-based injury risk surveillance. This review synthesises current evidence on artificial intelligence (AI)-based movement analysis and wearable sensor systems for predicting upper-limb overuse injury in wheelchairdependent para athletes. We examine the epidemiological and biomechanical rationale for shoulder and wrist vulnerability, the sensor modalities and AI architectures reported in the literature, and the reported predictive performance of these systems, before discussing the translational implications for sports physiotherapy practice, including workload monitoring, technique correction, and early referral pathways. The review identifies persistent gaps — a scarcity of studies conducted specifically in wheelchair-sport (rather than able-bodied or activities-of-daily-living) populations, limited external validation, heterogeneous outcome definitions, and minimal integration into physiotherapist-facing clinical workflows — and proposes a research and implementation agenda to close them.