arxiv: 2605.00879 · v1 · submitted 2026-04-26 · 💻 cs.RO · cs.SY· eess.SY

Recognition: unknown

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

Soumia Siyoucef , Najmeddine Dhieb , Hakim Ghazzai , Eleonora Guanziroli , Franco Molteni , Gianluca Setti

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:30 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY

keywords LiDARrehabilitationgait analysis3D body scanningrobotic systemsactivity recognitionAI techniquessurvey

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The pith

This survey provides the first comprehensive overview of LiDAR applications in rehabilitation along with AI processing techniques and open challenges from 2019 to 2025 studies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews how LiDAR sensors support rehabilitation by enabling real-time monitoring of patient movements and accurate feedback on exercise performance. It explores applications including standalone 3D body scanning for gait analysis, LiDAR mounted on robotic systems, environment scanning for safe navigation, and activity recognition. The authors examine learning-based AI methods for handling the sensor data and present statistical trends along with identified gaps. A reader would care because LiDAR avoids the privacy problems of camera systems and the discomfort or inaccuracy of wearable sensors. The work positions itself as the initial dedicated survey to map current methods and point toward future opportunities in post-injury care and hospital settings.

Core claim

LiDAR has demonstrated strong potential for rehabilitation by offering non-contact monitoring that provides accurate feedback on movement accuracy. Applications reviewed include 3D body scanning and gait analysis with standalone sensors, integration with robotic rehabilitation systems, real-time environment scanning for navigation safety, and activity and position recognition. The survey analyzes AI-based processing techniques, supports the discussion with statistical analysis of trends, and identifies gaps and future research directions, claiming to be the first comprehensive review dedicated to this topic for studies published between 2019 and 2025.

What carries the argument

The survey's structured review and categorization of LiDAR rehabilitation uses across standalone scanning, robotic mounting, navigation assistance, and recognition tasks, combined with analysis of learning-based data processing methods.

If this is right

Rehabilitation programs can adopt LiDAR for non-contact movement tracking that eliminates camera privacy concerns and wearable sensor discomfort.
Learning-based AI techniques can process LiDAR point clouds to deliver precise real-time feedback on exercise accuracy during therapy sessions.
Robotic rehabilitation platforms gain improved safety and patient positioning through integrated LiDAR environment scanning.
Statistical trends in the reviewed studies point to increasing use of AI methods and highlight needs for expanded applications in hospital and home settings.
Identified open challenges direct future work toward better integration of LiDAR in post-injury care and clinical environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Hybrid sensor setups pairing LiDAR with other modalities could address remaining limitations in movement capture for complex patient cases.
Controlled trials comparing LiDAR-assisted rehab outcomes directly to traditional methods would provide quantitative evidence of clinical benefits.
Deployment in home-based therapy programs might reduce clinic visits while maintaining monitoring quality for chronic mobility conditions.
Standardized benchmarks for LiDAR data accuracy in gait and activity tasks would help compare performance across different rehabilitation devices.

Load-bearing premise

The selected studies from the 2019-2025 literature search form a representative and unbiased picture of LiDAR use in rehabilitation without significant omissions or selection bias.

What would settle it

A new search or study identifying multiple major LiDAR rehabilitation papers published before 2019 or after the cutoff that change the reported trends, gaps, or advantages would undermine the survey's claim of being the first comprehensive overview and its assessment of the technology's potential.

Figures

Figures reproduced from arXiv: 2605.00879 by Eleonora Guanziroli, Franco Molteni, Gianluca Setti, Hakim Ghazzai, Najmeddine Dhieb, Soumia Siyoucef.

**Figure 2.** Figure 2: On the left, a single laser beam emitted by the LiDAR hits the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Comparison between LiDAR and camera-based sensing systems: [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Overall pipeline of LiDAR point cloud processing, including preprocessing, feature extraction, key joint estimation, segmentation, and subsequent [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Key gait parameters including step width, stride length, step length, [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: LiDAR sensor mounted on a mobile robot that follows the patient’s trajectory while analyzing gait patterns [29]. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Automated Wheelchair Docking to a Body-Separated Nursing Bed [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Generated human skeleton using 3D LiDAR point cloud data. The [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗

**Figure 9.** Figure 9: Overview of the LiDAR-based patient activity recognition frame [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗

**Figure 11.** Figure 11: Percentage of studies using AI and non-AI approaches in relation to [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗

**Figure 12.** Figure 12: AI vs. non-AI approaches (2019–2024) in LiDAR-based health [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This survey organizes LiDAR uses across rehab applications but the 'first comprehensive' claim cannot be checked without any search methodology.

read the letter

The main point is that this is a literature survey pulling together work on LiDAR for rehabilitation from 2019 to 2025. It groups studies into areas like standalone 3D scanning and gait analysis, LiDAR mounted on robots, environment monitoring for navigation, and activity recognition, then reviews AI processing methods and adds some trend statistics plus future directions. That organization is the useful part. It gives someone new to the topic a map of how this privacy-friendly sensor is being tried in clinical settings where cameras raise consent issues and wearables can slip or irritate patients. The synthesis across sub-areas and the focus on learning-based techniques are the parts that feel like they could save a reader time. The claim that this is the first dedicated survey on the topic is the element that stands out as new, assuming the selection really covers the scattered papers. The soft spot is exactly what the stress test flagged: the abstract and available text give no databases, no search strings, no inclusion or exclusion rules, and no PRISMA-style diagram. Without those, it is impossible to know whether the 2019-2025 window missed earlier foundational LiDAR work or skewed toward certain applications like gait over robotic mounting. The statistical trends are mentioned but cannot be evaluated for rigor from what is shown. This paper is for researchers or clinicians entering the intersection of sensors and rehab medicine who want a starting reference rather than new algorithms or data. It is not aimed at general robotics or core AI audiences. I would bring it to a reading group only if the group focuses on applied healthcare sensing. I would not cite it in my own work. It deserves peer review once the authors add a clear methods section on the literature search; that single addition would make the coverage claims verifiable and turn it into a usable reference for the subfield.

Referee Report

1 major / 1 minor

Summary. The manuscript is a survey paper claiming to be the first comprehensive review of LiDAR applications in rehabilitation. It covers studies from 2019 to 2025 on topics including 3D body scanning and gait analysis using standalone LiDAR, LiDAR on robotic systems, real-time monitoring and environment scanning for navigation, and activity/position recognition. The paper also discusses AI-based processing techniques and includes statistical analysis to highlight trends, gaps, and future directions.

Significance. If the survey methodology ensures a representative sample, the paper could serve as a useful reference for researchers in robotics and rehabilitation, synthesizing advantages of LiDAR over privacy-invasive cameras and uncomfortable wearables, and pointing to open challenges in the field.

major comments (1)

Abstract: The central claim that 'this is the first comprehensive survey dedicated to LiDAR for rehabilitation applications' is undermined by the absence of any description of the literature search methodology. The abstract mentions the 2019-2025 date range and topic areas but provides no information on databases searched, search keywords or strings, inclusion/exclusion criteria, number of papers initially found versus included, or a PRISMA-style diagram. This information is essential to substantiate the comprehensiveness and lack of bias in the selection.

minor comments (1)

Abstract: The abstract states that the discussion is supported by 'statistical analysis' but does not specify what quantitative methods or metrics are used (e.g., publication counts per year, performance comparisons). This could be clarified in the main text for better transparency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our survey manuscript. We address the major comment point by point below and will revise the manuscript to incorporate the suggested improvements for greater transparency.

read point-by-point responses

Referee: Abstract: The central claim that 'this is the first comprehensive survey dedicated to LiDAR for rehabilitation applications' is undermined by the absence of any description of the literature search methodology. The abstract mentions the 2019-2025 date range and topic areas but provides no information on databases searched, search keywords or strings, inclusion/exclusion criteria, number of papers initially found versus included, or a PRISMA-style diagram. This information is essential to substantiate the comprehensiveness and lack of bias in the selection.

Authors: We agree that a detailed description of the literature search methodology is necessary to rigorously support the claim of providing the first comprehensive survey and to demonstrate transparency in paper selection. The current version states the 2019-2025 timeframe and covered topics but does not elaborate on the search process. In the revised manuscript, we will add a new 'Literature Search Methodology' subsection in the Introduction. This will specify the databases queried (IEEE Xplore, Scopus, Web of Science, PubMed, Google Scholar, and arXiv), the search strings and Boolean combinations used (e.g., 'LiDAR' AND ('rehabilitation' OR 'gait analysis' OR '3D body scanning' OR 'activity recognition' OR 'fall detection')), inclusion criteria (peer-reviewed English-language studies from 2019-2025 applying LiDAR in rehabilitation, post-injury care, or hospital settings), exclusion criteria (non-English papers, purely theoretical works, reviews, or studies without empirical LiDAR use), and quantitative results (initial retrieval count, duplicates removed, full-text screening, and final included papers). We will also include a PRISMA-style flow diagram to visualize the selection process. These additions will directly address the concern without changing the survey's core findings or scope. revision: yes

Circularity Check

0 steps flagged

No circularity: survey summarizes external literature without derivations or self-referential claims

full rationale

The paper is a literature survey reviewing published studies on LiDAR in rehabilitation (2019-2025). It contains no equations, fitted parameters, predictions, or derivations that could reduce to inputs by construction. The novelty claim ('first comprehensive survey') is a standard scoping statement resting on the authors' external search of prior work, not on any self-definition, self-citation chain, or renaming of results. No patterns from the enumerated list apply; the paper is self-contained as a review of independent sources.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey paper the central claim rests on completeness of literature coverage rather than new parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5548 in / 1121 out tokens · 25532 ms · 2026-05-09T20:30:08.035758+00:00 · methodology

discussion (0)

Reference graph

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