WiLife: Long-Term Daily Status Monitoring and Habit Mining of the Elderly Leveraging Ubiquitous Wi-Fi Signals

The global aging demographic underscores the imperative for continuous in-home monitoring of the empty-nest elderly, ensuring their safety and well-being. The widespread deployment of Wi-Fi infrastructure has paved the way to monitor the elderly in a non-intrusive and privacy-preserving manner. Numerous studies have explored the potential of utilizing Wi-Fi signals to address urgent life safety concerns such as fall detection and vital sign monitoring. However, apart from these acute safety issues, the early detection of potential disease symptoms and managing the progression of chronic diseases are also crucial for elderly care, which calls for long-term and continuous monitoring of the elderly’s daily routines. Unfortunately, challenges like continuous activity segmentation and location/orientation dependencies have hindered the implementation of a long-term, around-the-clock activity monitoring system for the elderly. This work introduces “WiLife,” a cutting-edge Wi-Fi-based framework for continuous monitoring of the elderly’s spatio-temporal daily status information. Specifically, WiLife adopts a strategy of partitioning living spaces into functional areas and categorizing daily activities into atomic states. By encapsulating daily life status into a unique series of triple unit format: \(\left\langle\textit{Time, Area, State}\right\rangle\), WiLife is able to offer valuable insights into when, where, and how activities occur. Field implementations spanning 1,080 hours (45 days \(\times\) 24 hours) in real-world home environments highlight WiLife’s exceptional capability in understanding individual living habits and timely detection of irregularities.