Wi-Fi-based physiological examination of psychiatric patients
Depression seriously endangers the health of patients, and impairs their work, study and social functions, even leads to suffering, despair and suicide. Statistics show that there are close to 100 million depression patients in China, and more than 200,000 people commit suicide due to depression each year.
Depression has gradually developed into a serious social problem. However, the current depression evaluation is mainly based on psychiatrists’ subjective examinations and scales, which lack objective indicators and are easy to be misdiagnosed. Thus, there is an urgent need for indicators that can objectively and quantitatively assess the depression.
Due to its ubiquity, no-invasion, fine granularity and privacy protection, Wi-Fi sensing technology has achieved great success human perception and highlights huge potential in the medical monitoring field. This project will combine Wi-Fi sensing with depression evaluation to investigate objective physiological indicators for the depression evaluation and realize Wi-Fi-based detection of depression patients, which can provide strong support for timely detection and accurate diagnosis of depression patients.
Monitoring of elderly living alone
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
This work presents an in-home monitoring approach based on offthe-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.
 Lingchao Guo, Zhaoming Lu, Shuang Zhou, Xiangming Wen, Zhihong He. Emergency Semantic Feature Vector Extraction from WiFi Signals for In-Home Monitoring of Elderly. in IEEE Journal of Selected Topics in Signal Processing, (under review).