Two decades of AI-driven motion capture in rehabilitation: Mapping research networks, thematic hotspots, and future trajectories

Background Motion capture technology integration in artificial intelligence (AI)-driven rehabilitation represents a rapidly expanding interdisciplinary field with significant potential for advancing movement analysis and motor recovery. A comprehensive bibliometric mapping of this domain is currently lacking, limiting systematic understanding of its development trajectory and key contributors. Objective To provide a WoS-indexed bibliometric analysis of AI applications in motion capture for rehabilitation, identifying research trends, collaboration networks, key contributors, and emerging research frontiers from 2004 to 2023. Methods A total of 3,500 relevant publications indexed in the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases were retrieved and analyzed. Bibliometric and visualization analyses were performed using VOSviewer (version 1.6.19) and CiteSpace (version 6.2.4) to map collaboration networks, co-citation relationships, and keyword co-occurrence patterns. Results Annual publication output demonstrated consistent growth from 2004 to 2023, with cumulative publications exceeding 3,500. The United States (1,014 publications) and China (722 publications) dominated research output, although collaboration patterns differed substantially. The University of Chinese Academy of Sciences led institutional contributions (52 publications). Keyword clustering revealed prominent research themes centered on brain-computer interfaces, machine learning, EEG-based signal processing, and real-time rehabilitation feedback systems. Temporal analysis demonstrated a paradigm shift from fundamental neurophysiological investigations toward computationally-driven and AI-integrated rehabilitation frameworks. Conclusions This bibliometric analysis provides a WoS-indexed mapping of AI-driven motion capture research in rehabilitation. The identified research hotspots and collaboration patterns offer a foundational reference for future investigations, despite limitations related to database coverage and language scope. Continued interdisciplinary collaboration and standardized methodological frameworks are essential to accelerate clinical translation.