Syncope Detection in Toilet Environments Using Wi-Fi Channel State Information

Ziqi Wang, Zhihao Gu, Junwei Yin, Zhe Chen, Yuedong Xu

Abstract: Syncope and strokes in toilets can lead to severe injuries, and even pose life threats to patients. However, owing to privacy concerns, vision-based fall detection cannot be applied in such a scenario. In this poster, we propose ToiFall, a prototype for syncope detection in toilet environments. ToiFall collects Channel State Information (CSI) of commodity Wi-Fi devices. Different human movements form various textures on CSI images, and such textures can be used for feature extraction and classification. Experimental results show an accuracy of over 98% for fall detection with satisfying reliability.