Wearable sensors, positioning systems, and computer vision for beach volleyball athlete monitoring: a systematic review

Abstract

Beach volleyball is contested on deformable sand under variable outdoor conditions, creating movement and load demands that differ from indoor volleyball and may challenge the assumptions of athlete-monitoring technologies. This systematic review mapped technology-based monitoring systems used in beach volleyball and synthesised evidence on their validity, reliability, applications, limitations, and research gaps. The review followed PRISMA 2020 and searched Scopus using a structured Boolean strategy. Of 403 records identified, 54 full texts were assessed and 22 peer-reviewed studies published from 2014 to 2026 were included. Six monitoring modalities were identified: satellite/local positioning systems, wearable inertial measurement units, computer vision, machine-learning analytics, video-based mobile applications, and wearable eye-tracking. Validation evidence was heterogeneous and was reported only where individual studies evaluated measurement properties. In official competition, an inertial device achieved 96.29% overall jump-detection sensitivity, while another IMU study reported jump-count precision of 0.975 and recall of 0.836–1.000 depending on jump type. For the My Jump 2 application, reliability ranged from ICC 0.91–0.97 under controlled sand-box conditions but only ICC 0.447–0.594 in open-field sand conditions. Direct comparison of GNSS and ultra-wideband systems also showed significant differences across kinematic outputs, indicating that their values should not be treated as interchangeable. The evidence therefore supports descriptive profiling and role-specific load monitoring, but not yet universal thresholds, cross-device benchmarking, or injury prediction. Future research should prioritise multi-database evidence retrieval, independent multi-reviewer screening, sex- and action-stratified validation, prospective injury surveillance, and multimodal integration.