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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/60668
Title: Application of a nonparametric gait model and feature generation based on insole data
Authors: Li, H.
Tsviatkou, V. Yu.
Keywords: материалы конференций;feature generation;kinesics;Berg balance scale
Issue Date: 2025
Publisher: БГУИР
Citation: Li, H. Application of a nonparametric gait model and feature generation based on insole data / H. Li, V. Yu. Tsviatkou // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, апрель 2025 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. Ю. Цветков [и др.]. – Минск, 2025. – С. 126–129.
Abstract: Gait and balance disorders pose significant risks of injury and negatively impact quality of life, especially among the elderly and individuals with neurological conditions. In this work, a novel feature generation method based on a nonparametric gait model (NPWM) for plantar pressure data is proposed for early fall risk assessment. Unlike traditional gait analysis methods that rely on fixed parameters or predefined models, the proposed approach directly extracts spatiotemporal features from real world data collected by wearable plantar pressure sensors. These features include raw spatial distributions, gait temporal characteristics, balance stability metrics, energy expenditure features, and informative feature ratios. Experimental results demonstrate that the proposed method achieves a fall risk prediction accuracy of 0,9, offering significant advantages over conventional clinical assessment methods.
URI: https://libeldoc.bsuir.by/handle/123456789/60668
Appears in Collections:Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2025)

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