Artificial intelligence enabled wearable technologies in beach volleyball: a systematic literature review of current evidence, research gaps, and future directions

Abstract

The fusion of artificial intelligence (AI) and wearable technology has transformed athlete monitoring, yet evidence in volleyball, particularly beach volleyball, remains fragmented across engineering, computer science, and sports medicine. This systematic literature review (SLR) synthesized peer-reviewed studies on AI-enabled wearable devices for volleyball performance, training load, and injury monitoring, emphasizing the applicability of indoor volleyball findings to beach volleyball. Following PRISMA 2020 guidelines, two independent reviewers conducted study selection and data extraction, achieving excellent inter-rater reliability (Cohen's κ = 0.87). Methodological quality was assessed using the JBI Critical Appraisal Checklist, and findings were synthesized thematically. A Scopus search identified 102 records; after duplicate removal and screening, 10 studies met the eligibility criteria. Eligible studies were English-language journal articles published between 2018 and 2025 involving wearable sensing and computational or machine-learning applications in volleyball. Four themes emerged: deep learning combined with inertial measurement units accurately recognized volleyball activities and jump types; wearable-derived training load predicted injury risk and performance using personalized algorithms; sensor-based skill assessment was validated mainly indoors; and beach-specific technologies remained limited, except for environmental monitoring. Overall, AI-enabled wearable systems demonstrated high recognition accuracy, but evidence for external validation and beach-specific applications remains scarce. This review provides the first beach-oriented roadmap for future research on wearable AI in volleyball.

Keywords
  • Artificial intelligence
  • Wearable sensors B
  • each volleyball
  • Athlete monitoring
  • Sports biomechanics
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