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SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis

Dario Sipari Orcid Logo, Betsy D. M. Chaparro-Rico Orcid Logo, Daniele Cafolla Orcid Logo

International Journal of Environmental Research and Public Health, Volume: 19, Issue: 16, Start page: 10032

Swansea University Author: Daniele Cafolla Orcid Logo

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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...

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Published in: International Journal of Environmental Research and Public Health
ISSN: 1660-4601
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62492
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first_indexed 2023-03-01T16:55:42Z
last_indexed 2023-03-02T04:17:36Z
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spelling 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
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
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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score 11.013148

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