Abstract: The fall down behavior of elderly people in the bathroom poses a risk of serious harm due to poor timely rescue.Therefore,efficient and rapid monitoring of fall down in toilet is of great significance.A non-contact fall down in toiletmonitoring model WiCare,which integrates convolutional neural network(CNN),Bi-directional long short-term memory(BiLSTM),and self-attention mechanism,is proposed to address the issues of insufficient feature extraction and limited monitoring accuracy in current fall monitoring methods based on Wi-Fi perception,which are greatly affected by noise.Firstly,the amplitude is extracted from the original CSI data as the basic data.Secondly,multi-level discrete wavelet transform and soft threshold processing are used to reduce perceived data noise.Then,the perceptual data is reconstructed in multiple dimensions to more accurately characterize the characteristics of fall behavior.Finally,WiCare is used to extract effective features in the perception data,and then realize the function of monitoring toilet fall behavior in the toilet.Experimental results show that the accuracy of WiCare in monitoring fall behavior in the home bathroom environment is 99.41%.Compared with other similar models,WiCare has high recognition accuracy,low model complexity,and stronger generalization ability.

Key words: Wi-Fi sensing, Fall down in toilet detection, Multilevel discrete wavelet transform, Soft threshold processing, Deep learning