ArticleDigital health
Two decades of AI-driven motion capture in rehabilitation: Mapping research networks, thematic hotspots, and future trajectories.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.