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Portable gait-asymmetry detection using lowcost hardware and machine learning
Citation Link: https://doi.org/10.15480/882.16224
Publikationstyp
Journal Article
Date Issued
2025-09-01
Sprache
English
Author(s)
TORE-DOI
Volume
11
Issue
1
Start Page
540
End Page
543
Citation
Current Directions in Biomedical Engineering 11 (1): 540-543 (2025)
Publisher DOI
Publisher
Walter de Gruyter GmbH
Gait analysis provides insights into human motion by examining how individuals walk. However, the high cost associated with gait centers prevents conducting gait analysis regularly, reducing opportunities for early detection and prevention of gait-related issues before pain or injuries occur. The approach presented in this paper integrates low-cost yet computationally powerful hardware with signal processing and machine learning to develop a wearable sensor node placed at the pelvis that continuously collects gait data, providing personalized gait analysis. By positioning the wearable at the pelvis, gait asymmetries can be captured accurately. The approach is validated in a laboratory experiment with 15 participants walking on a treadmill and verified in a free-moving environment. Results indicate that the wearable detects gait asymmetries effectively, enhancing applicability in both clinical and non-clinical settings, supporting rehabilitation and preventive care in a cost-efficient manner.
Subjects
Gait analysis
machine learning
wearable technology
Internet of Things
DDC Class
616.07: Pathology
Publication version
publishedVersion
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Name
10.1515_cdbme-2025-0237-1.pdf
Type
Main Article
Size
1.5 MB
Format
Adobe PDF