Real-time fall detection system based on postural changes

Mingxiang Chen; Huanyu Liu; Mengyang Gao; Yu Wang

doi:10.1117/12.3015382

15 March 2024 Real-time fall detection system based on postural changes

Mingxiang Chen, Huanyu Liu, Mengyang Gao, Yu Wang

Proceedings Volume 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023); 130790I (2024) https://doi.org/10.1117/12.3015382
Event: 3rd International Conference of Testing Technology and Automation Engineering (TTAE 2023), 2023, Xi-an, China

Abstract

To enable real-time detection of falls among elderly individuals, this paper presents a fall detection system based on the dynamic changes in human body posture. This system employs inertial sensors to collect posture data from elderly individuals. After preprocessing and feature extraction, the system utilizes the ReliefF algorithm to determine feature weights. Based on this, it establishes a fall risk index. Subsequently, four Support Vector Machine (SVM) models with different kernel functions are trained. The predictions from these models are then combined using a voting mechanism and integrated with genetic algorithms, resulting in a multi-kernel SVM ensemble model. Finally, a comprehensive algorithmic model for fall detection is established. Through experimental testing, this comprehensive algorithmic model achieves an accuracy rate of 99.3%, thereby confirming the feasibility of the system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingxiang Chen, Huanyu Liu, Mengyang Gao, and Yu Wang "Real-time fall detection system based on postural changes", Proc. SPIE 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023), 130790I (15 March 2024); https://doi.org/10.1117/12.3015382

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