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

2026-04-26

AI Summary

This study addresses the limitations of conventional rehabilitation monitoring methods—such as privacy concerns with cameras and discomfort or measurement inaccuracies associated with wearable devices—by providing a systematic review of LiDAR applications in rehabilitation from 2019 to 2025. It comprehensively examines LiDAR’s deployment in 3D human body scanning, gait analysis, robot-assisted rehabilitation, environmental perception, and activity recognition. The review integrates AI-driven approaches, particularly deep learning techniques for point cloud processing, pose estimation, and action recognition. By synthesizing key technical pathways and empirical outcomes, this work elucidates current research gaps and clarifies LiDAR’s advantages, limitations, and promising future directions for enabling unobtrusive, high-precision rehabilitation monitoring, thereby offering a foundational reference for advancing the field.

Abstract

Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other therapeutic methods. Recent advances in technology have introduced sensor-based systems into rehabilitation and clinical practices, enabling real-time monitoring and providing accurate feedback on movement accuracy. Among these sensors, LiDAR has demonstrated strong potential, offering key advantages over conventional techniques such as camera-based systems, which raise privacy concerns, and wearable sensors, which can be uncomfortable and prone to errors. In this work, we review the applications of LiDAR in rehabilitation, post-injury care, and hospital environments, focusing on studies published between 2019 and 2025. Studies across several areas have been explored: 3D body scanning and gait analysis with standalone LiDAR, LiDAR mounted on robotic systems for rehabilitation, real-time monitoring and environment scanning for safe navigation, and activity and position recognition. We also analyze processing techniques, particularly learning-based approaches, and support the discussion with statistical analysis, highlighting trends, gaps, and future research opportunities. To the best of our knowledge, this is the first comprehensive survey dedicated to LiDAR for rehabilitation applications, providing an overview of current methods, AI-based processing techniques, and open challenges.

Problem

• LiDAR
• rehabilitation
• motion monitoring
• privacy-preserving sensing
• sensor-based rehabilitation

Innovation

• LiDAR
• rehabilitation
• AI techniques
• gait analysis
• non-invasive sensing