  As elderly population grows, social and health care begin to face validation
challenges, in-home monitoring is becoming a focus for professionals in the
field. Governments urgently need to improve the quality of healthcare services
at lower costs while ensuring the comfort and independence of the elderly. This
work presents an in-home monitoring approach based on off-the-shelf WiFi, which
is low-costs, non-wearable and makes all-round daily healthcare information
available to caregivers. The proposed approach can capture fine-grained human
pose figures even through a wall and track detailed respiration status
simultaneously by off-the-shelf WiFi devices. Based on them, behavioral data,
physiological data and the derived information (e.g., abnormal events and
underlying diseases), of the elderly could be seen by caregivers directly. We
design a series of signal processing methods and a neural network to capture
human pose figures and extract respiration status curves from WiFi Channel
State Information (CSI). Extensive experiments are conducted and according to
the results, off-the-shelf WiFi devices are capable of capturing fine-grained
human pose figures, similar to cameras, even through a wall and track accurate
respiration status, thus demonstrating the effectiveness and feasibility of our
approach for in-home monitoring.
