[2605.00879] LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions
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Computer Science > Robotics
arXiv:2605.00879 (cs)
[Submitted on 26 Apr 2026]
Title:LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions
Authors:Soumia Siyoucef, Najmeddine Dhieb, Hakim Ghazzai, Eleonora Guanziroli, Franco Molteni, Gianluca Setti
View a PDF of the paper titled LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions, by Soumia Siyoucef and 5 other authors
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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.
Comments: This paper is accepted for publication in IEEE Sensors Reviews, April, 2026
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2605.00879 [cs.RO]
(or arXiv:2605.00879v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.00879
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Najmeddine Dhieb [view email] [v1] Sun, 26 Apr 2026 08:59:11 UTC (7,842 KB)
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