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Artificial intelligence (AI) has rapidly reshaped sports motion analysis through advances in wearable sensing, computer vision, and deep learning. However, existing reviews often focus on isolated techniques and lack a systematic evolutionary perspective. This study presents a 15-year bibliometric and evolutionary review of AI in sports motion analysis (2011–2025), integrating scientometric mapping with quantitative content analysis across modalities, methods, and applications. A comprehensive multi-source dataset of 2602 publications was analyzed using VOSviewer and CiteSpace to examine knowledge structures, collaboration patterns, co-citation networks, and emerging research fronts. In addition, each study was categorized by modality (wearable, visual, and multi-modal), AI method (traditional machine learning, deep learning, and transformer-based), and task type (activity recognition, performance analysis, rehabilitation, and others). The results reveal exponential growth and a clear three-stage evolution: a sensor-driven phase dominated by wearable devices and traditional machine learning (2011–2015), a deep learning expansion phase centered on vision-based modeling (2016–2019), and a recent deep learning consolidation phase characterized by emerging transformer-based methods, increasing multimodal integration, real-time monitoring, and application-oriented sports analytics (2020–2025). The field has shifted from signal-based recognition toward vision-centered performance evaluation, rehabilitation-related movement assessment, and injury-informed applications. This data-driven review provides an integrated evolutionary framework and future research roadmap to support the continued development of AI-driven analytics in sports science, athlete monitoring, and health-related human movement analysis.
Hu et al. (2026) studied this question.