Following the aging society and the expanding demand for health care, artificial intelligence assistive health care has become a trend. However, the privacy concern on image portrait and delivery is of an important issue for care centers. Therefore, sensing devices such as radar, iToF and Lidar are used instead of RGB camera on image formation, which can ease the portrait privacy concerns, detect behaviors and physiology, return the falling status and physiological information in real time, reduce the manpower to measurement and care time, and reduce the risk while the patients are unattended.

Fall detection uses depth (Lidar, iToF) sensing device to generate a field depth map, judge portrait behavior, identify fall action, and distinguish similar behavior to reduce misjudgment, so as to achieve the purpose of immediate return and reduce the number of inspections of nursing manpower.

Physiological induction detection detects respiratory and heartbeat remotely through radar, which can still be implemented in private places as well as bathrooms and other specific fields without using wearable devices, so that there is no dead angle in the care space and risks are greatly reduced.