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SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis
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Betsy D. M. Chaparro-Rico ,
Daniele Cafolla
International Journal of Environmental Research and Public Health, Volume: 19, Issue: 16, Start page: 10032
Swansea University Author:
Daniele Cafolla
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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 |
| Published: |
MDPI AG
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62492 |
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| 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 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. |
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| Keywords: |
human biomechanics; automated gait analysis; artificial intelligence; motion tracking; markerless |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was funded by a grant from Ministero della Salute (Ricerca Corrente 2022). |
| Issue: |
16 |
| Start Page: |
10032 |