Remote Gait Type Classification System Using Markerless 2D Video
Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/10/1824 |
| _version_ | 1797514902351380480 |
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| author | Pedro Albuquerque João Pedro Machado Tanmay Tulsidas Verlekar Paulo Lobato Correia Luís Ducla Soares |
| author_facet | Pedro Albuquerque João Pedro Machado Tanmay Tulsidas Verlekar Paulo Lobato Correia Luís Ducla Soares |
| author_sort | Pedro Albuquerque |
| collection | DOAJ |
| description | Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics. |
| first_indexed | 2024-03-10T06:38:07Z |
| format | Article |
| id | doaj.art-fa87c67e03794201a98348c25b5d67a3 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T06:38:07Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-fa87c67e03794201a98348c25b5d67a32023-11-22T17:57:18ZengMDPI AGDiagnostics2075-44182021-10-011110182410.3390/diagnostics11101824Remote Gait Type Classification System Using Markerless 2D VideoPedro Albuquerque0João Pedro Machado1Tanmay Tulsidas Verlekar2Paulo Lobato Correia3Luís Ducla Soares4Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, PortugalInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa, PortugalDepartment of CSIS and APPCAIR, BITS Pilani, K K Birla, Goa Campus, Goa 403726, IndiaInstituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, PortugalInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa, PortugalSeveral pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.https://www.mdpi.com/2075-4418/11/10/1824assisted livinggait classificationpathology identificationremote diagnosisweb application |
| spellingShingle | Pedro Albuquerque João Pedro Machado Tanmay Tulsidas Verlekar Paulo Lobato Correia Luís Ducla Soares Remote Gait Type Classification System Using Markerless 2D Video Diagnostics assisted living gait classification pathology identification remote diagnosis web application |
| title | Remote Gait Type Classification System Using Markerless 2D Video |
| title_full | Remote Gait Type Classification System Using Markerless 2D Video |
| title_fullStr | Remote Gait Type Classification System Using Markerless 2D Video |
| title_full_unstemmed | Remote Gait Type Classification System Using Markerless 2D Video |
| title_short | Remote Gait Type Classification System Using Markerless 2D Video |
| title_sort | remote gait type classification system using markerless 2d video |
| topic | assisted living gait classification pathology identification remote diagnosis web application |
| url | https://www.mdpi.com/2075-4418/11/10/1824 |
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