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Review
. 2025 Jan 16;25(2):498.
doi: 10.3390/s25020498.

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis

Xiaofeng Han  1 , Diego Guffanti  2 , Alberto Brunete  1
Affiliations
Review

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis

Xiaofeng Han et al. Sensors (Basel). .

Abstract

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

Keywords: 3D camera; gait parameters; human gait analysis; machine learning algorithms; mobile robot; visual sensors.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The number of articles published annually on the subject of human gait analysis between the years 2000 and 2024, as collated from the Scopus database.
Figure 2
Figure 2
The systematic selection procedure of the studied articles based on PRISMA search strategies. At first, a list of 1727 articles was obtained through a search string from the IEEE Xplore, PubMed, Web of Science, Scopus databases. Finally, 72 articles were selected through the PRISMA steps.
Figure 3
Figure 3
Trends in the number of relevant articles on human gait analysis (2019–2024).
Figure 4
Figure 4
The most frequent phrases in these 72 articles.
Figure 5
Figure 5
Categories of gait parameters in vision-based analysis.
See this image and copyright information in PMC

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