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As elderly population grows, social and health care begin to face validationchallenges, in-home monitoring is becoming a focus for professionals in thefield. Governments urgently need to improve the quality of healthcare servicesat lower costs while ensuring the comfort and independence of the elderly. Thiswork presents an in-home monitoring approach based on off-the-shelf WiFi, whichis low-costs, non-wearable and makes all-round daily healthcare informationavailable to caregivers. The proposed approach can capture fine-grained humanpose figures even through a wall and track detailed respiration statussimultaneously by off-the-shelf WiFi devices. Based on them, behavioral data,physiological data and the derived information (e.g., abnormal events andunderlying diseases), of the elderly could be seen by caregivers directly. Wedesign a series of signal processing methods and a neural network to capturehuman pose figures and extract respiration status curves from WiFi ChannelState Information (CSI). Extensive experiments are conducted and according tothe results, off-the-shelf WiFi devices are capable of capturing fine-grainedhuman pose figures, similar to cameras, even through a wall and track accuraterespiration status, thus demonstrating the effectiveness and feasibility of ourapproach for in-home monitoring.

When Healthcare Meets Off-the-Shelf WiFi: A Non-Wearable and Low-Costs Approach for In-Home Monitoring