The CSAIL team has already used the system in hospitals and assisted living facilities to monitor people for issues including Parkinson’s, dementia and COVID-19. The researchers have improved the system, which uses deep machine learning. It can identify activities, such as sleeping, reading, cooking and watching TV, and items like laptops. RF-Diary is accurate in classifying more than 30 household activities over 90 percent of the time, according to the researchers.
It uses a floor map of a subject’s living space to determine what actions they undertake in different parts of the home, and what objects they use to do so. To set up the system, the person who RF-Diary is monitoring has to carry out several actions. The system will also observe them walking around their living space to make sure it doesn’t monitor any locations the person doesn’t have access to, since radio signals can travel through walls.
Beyond protecting privacy, testing showed that the system is more effective at tracking someone’s activities in dark and “occluded” settings than video-based systems. Radio signals don’t need light, after all. The researchers plan to adapt the system for homes and hospitals, with the aim of selling it commercially.