Two decades of AI-driven motion capture in rehabilitation: Mapping research networks, thematic hotspots, and future trajectories
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By
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Xiaojing Huang
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Jing Xu
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Lingyan Chen
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June 16, 2026
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Clinical Scorecard: Twenty Years of AI-Enhanced Motion Capture in Rehabilitation: Analyzing Research Networks, Key Themes, and Future Directions
At a Glance
| Category | Detail |
|---|
| Condition | Rehabilitation using AI-driven motion capture technology |
| Key Mechanisms | Integration of AI algorithms for real-time data analysis and personalized rehabilitation strategies |
| Target Population | Individuals with motor impairments requiring rehabilitation |
| Care Setting | Interdisciplinary research environments including biomedical engineering, neuroscience, and physical therapy |
Key Highlights
- First bibliometric overview of AI-driven motion capture in rehabilitation
- Shift from neurophysiological studies to AI-integrated rehabilitation frameworks
- United States as a global research hub with China showing accelerated output
- Identified research hotspots in EEG, brain-computer interfaces, and multimodal sensing
- Mapping of collaboration networks and thematic evolution in the field
Guideline-Based Recommendations
Diagnosis
- Utilize AI algorithms for automated assessment of motor function
Management
- Implement personalized rehabilitation strategies informed by real-time data
Monitoring & Follow-up
- Employ motion capture systems for precise detection of human movement
Risks
- Consider the fragmented research landscape across multiple disciplines
Patient & Prescribing Data
Individuals with motor impairments
AI-driven methodologies are increasingly relevant in rehabilitation contexts
Clinical Best Practices
- Adopt interdisciplinary approaches combining technology and rehabilitation practices
- Focus on data-driven foundations for understanding AI-integrated motion capture research
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