MACHINE LEARNING-BASED ANALYSIS OF WEARABLE INERTIAL SENSOR SIGNALS FOR EARLY DETECTION OF GAIT ABNORMALITIES IN PHYSICAL REHABILITATION
DOI:
https://doi.org/10.48047/ehzgny53Keywords:
Wearable Sensors, Inertial Measurement Unit (IMU), Gait Analysis, Machine Learning, Physical Rehabilitation, Feature Extraction, Early Detection.Abstract
Gait assessment is an essential component of physical rehabilitation because alterations in walking patterns often indicate functional impairment, delayed recovery, or progression of musculoskeletal and neurological disorders. Conventional gait analysis systems, such as optical motion capture platforms, provide accurate measurements
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References
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A. Muro-de-la-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable and non-wearable systems,” Neurocomputing, vol. 74, no. 16, pp. 3362–3374, 2011.
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