FPGA-based fall detection robot for ice and snow sports venues

Meiqi Zeng; Zhoutao Bi; Jiaming Chen

doi:10.1117/12.3038075

7 August 2024 FPGA-based fall detection robot for ice and snow sports venues

Meiqi Zeng, Zhoutao Bi, Jiaming Chen

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322924 (2024) https://doi.org/10.1117/12.3038075
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

The 'Technology Winter Olympics' was a major highlight of the 2022 Beijing Winter Olympics, aimed at exploring better solutions for future urban living worldwide. This system, focusing on ice rinks as its primary application scenario, addresses the issue of elderly and children skaters who fall and cannot receive timely assistance due to mobility problems. The project utilizes programmable devices, FPGA (Field-Programmable Gate Array), to collect and process signals from multiple sensors, including angles, acceleration, and rotation speed; it implements fall detection through differential threshold method and uploads the results to a mobile app via 4G signal. The system's effectiveness was validated through online calibration in the ice rink, where, after about ten experimental tests, the fall detection success rate was 85%, and the success rate for other functionalities exceeded 90%. The results demonstrate that the system can accurately detect falls while ensuring high operational efficiency, and it holds potential for extensive application in public places for detecting falls among the elderly and children.

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

Citation Download Citation

Meiqi Zeng, Zhoutao Bi, and Jiaming Chen "FPGA-based fall detection robot for ice and snow sports venues", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322924 (7 August 2024); https://doi.org/10.1117/12.3038075

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