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SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis
International Journal of Environmental Research and Public Health, Volume: 19, Issue: 16, Start page: 10032
Swansea University Author: Daniele Cafolla
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© 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
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DOI (Published version): 10.3390/ijerph191610032
Abstract
The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the eff...
Published in: | International Journal of Environmental Research and Public Health |
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ISSN: | 1660-4601 |
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MDPI AG
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa62492 |
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2023-03-01T16:57:19.6946709 v2 62492 2023-02-03 SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis ac4feae4da44720e216ab2e0359e4ddb 0000-0002-5602-1519 Daniele Cafolla Daniele Cafolla true false 2023-02-03 SCS The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology. Journal Article International Journal of Environmental Research and Public Health 19 16 10032 MDPI AG 1660-4601 human biomechanics; automated gait analysis; artificial intelligence; motion tracking; markerless 14 8 2022 2022-08-14 10.3390/ijerph191610032 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This work was funded by a grant from Ministero della Salute (Ricerca Corrente 2022). 2023-03-01T16:57:19.6946709 2023-02-03T14:14:57.7776950 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Dario Sipari 0000-0001-9319-7540 1 Betsy D. M. Chaparro-Rico 0000-0002-6874-2508 2 Daniele Cafolla 0000-0002-5602-1519 3 62492__26721__457078f66ba04757a7d08b30b5fa99fe.pdf 62492_VoR.pdf 2023-03-01T16:56:16.0810860 Output 3328130 application/pdf Version of Record true © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ |
title |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
spellingShingle |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis Daniele Cafolla |
title_short |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
title_full |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
title_fullStr |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
title_full_unstemmed |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
title_sort |
SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis |
author_id_str_mv |
ac4feae4da44720e216ab2e0359e4ddb |
author_id_fullname_str_mv |
ac4feae4da44720e216ab2e0359e4ddb_***_Daniele Cafolla |
author |
Daniele Cafolla |
author2 |
Dario Sipari Betsy D. M. Chaparro-Rico Daniele Cafolla |
format |
Journal article |
container_title |
International Journal of Environmental Research and Public Health |
container_volume |
19 |
container_issue |
16 |
container_start_page |
10032 |
publishDate |
2022 |
institution |
Swansea University |
issn |
1660-4601 |
doi_str_mv |
10.3390/ijerph191610032 |
publisher |
MDPI AG |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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description |
The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology. |
published_date |
2022-08-14T04:22:09Z |
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1763754460429942784 |
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11.035349 |