Personal Hygiene Monitoring Under the Shower Using WiFi Channel State Information

Abstract

Personal hygiene is often used to measure functional independence, which is how much support someone requires to perform self-care. By extension, this is often used in the monitoring of (early-stage) dementia. Current technologies are based on either audiovisual or wearable technologies, both of which have practical limitations. The use of (NLOS) radio-frequency based human activity recognition could provide solutions here. This paper leverages the 802.11n channel state information to monitor different shower-related activities (e.g. washing head or body, brushing teeth, and dressing up) and the degree to which some of these can be monitored, as well estimating different water pressures used while showering for multiple locations in the apartment. Wavelet denoising is applied for filtering and a convolutional neural network is implemented for classification. Results imply that for coarse-grained activity recognition, an 퐹1-score of 0.85 is achievable for certain classes, while for fine-grained this drops to 0.75. Water pressure estimation ranges from 0.75 to 0.85 between fine-grained and coarse-grained, respectively. Overall, this paper shows that channel state information can be successfully employed to monitor variations in different shower activities, as well as successfully estimating the water pressure in the shower