Channel state information-based fall detection using IoT devices

Guanting Ye; Shaojing Zhang; Xinyu Qiu; Yuhui Zheng; Xiaona Guo; Shaojiang Liu

doi:10.1117/12.3010434

20 October 2023 Channel state information-based fall detection using IoT devices

Guanting Ye, Shaojing Zhang, Xinyu Qiu, Yuhui Zheng, Xiaona Guo, Shaojiang Liu

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 128141C (2023) https://doi.org/10.1117/12.3010434
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

The demand for fall detection technology in elderly individuals is increasing due to the growing global aging population. Wi-Fi's Channel State Information (CSI) based fall detection is gaining popularity as it does not require the installation of expensive cameras or sensors, is not affected by lighting and obstructions, and protects individual privacy. However, previous studies using commercial Wi-Fi and mobile hosts to acquire CSI and recognize falls are expensive. To address this issue, this paper proposes a low-cost fall detection solution based on ESP32-S3 and LSTM model. The proposed solution collects data from 52 subcarriers in the L-LTF, applies Butterworth low-pass filter, and PCA to reduce data dimensionality. The LSTM model achieves a testing accuracy of 96.03%, with 89% classification accuracy for falls. This solution can significantly reduce the cost of fall detection technology and has potential deployment on ESP32-S3. Future research could focus on improving accuracy or exploring different application scenarios.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guanting Ye, Shaojing Zhang, Xinyu Qiu, Yuhui Zheng, Xiaona Guo, and Shaojiang Liu "Channel state information-based fall detection using IoT devices", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 128141C (20 October 2023); https://doi.org/10.1117/12.3010434

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