A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis
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 th...
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MDPI AG
2025-01-01
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| author | Xiaofeng Han Diego Guffanti Alberto Brunete |
| author_facet | Xiaofeng Han Diego Guffanti Alberto Brunete |
| author_sort | Xiaofeng Han |
| collection | DOAJ |
| description | 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]. |
| format | Article |
| id | doaj-art-cd38ddde4b4547379b06f09f37865e23 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-cd38ddde4b4547379b06f09f37865e232025-01-24T13:49:09ZengMDPI AGSensors1424-82202025-01-0125249810.3390/s25020498A Comprehensive Review of Vision-Based Sensor Systems for Human Gait AnalysisXiaofeng Han0Diego Guffanti1Alberto Brunete2Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, SpainUniversidad UTE, Av. Mariscal Sucre, Quito 170129, EcuadorCentre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, SpainAnalysis 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].https://www.mdpi.com/1424-8220/25/2/498human gait analysisvisual sensorsmachine learning algorithmsgait parameters3D cameramobile robot |
| spellingShingle | Xiaofeng Han Diego Guffanti Alberto Brunete A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis Sensors human gait analysis visual sensors machine learning algorithms gait parameters 3D camera mobile robot |
| title | A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis |
| title_full | A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis |
| title_fullStr | A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis |
| title_full_unstemmed | A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis |
| title_short | A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis |
| title_sort | comprehensive review of vision based sensor systems for human gait analysis |
| topic | human gait analysis visual sensors machine learning algorithms gait parameters 3D camera mobile robot |
| url | https://www.mdpi.com/1424-8220/25/2/498 |
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