LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions

A new survey titled "LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions" has been published in IEEE Sensors Reviews (April 2026). The authors—Siyoucef, Dhieb, Ghazzai, Guanziroli, Molteni, and Setti—systematically review studies from 2019 to 2025. They argue that LiDAR (Light Detection and Ranging) sensors are a superior alternative to traditional camera-based systems (which raise privacy concerns) and wearable sensors (which can be uncomfortable and error-prone). LiDAR generates precise 3D point clouds without recording identifiable imagery, making it ideal for clinical and home rehabilitation settings.

The survey organizes applications into four categories: 3D body scanning and gait analysis using standalone LiDAR; LiDAR mounted on robotic systems for assisted rehabilitation; real-time monitoring and environment scanning for safe navigation; and activity and position recognition. The authors also analyze AI-based processing techniques—particularly deep learning and point cloud networks—and include statistical analysis of trends and research gaps. They highlight that this is the first dedicated survey on LiDAR for rehabilitation, noting open challenges such as sensor cost, data integration with electronic health records, and real-time processing on edge devices. The work provides a roadmap for future research in privacy-preserving, accurate patient monitoring.