Daily Activity Recognition based on Markov Logic Network for Elderly Monitoring
Yoshiki Honda, Hirozumi Yamaguchi, Teruo Higashino · 2019
In this paper, we propose a daily activity recognition Method using multiple low-cost infrared portable sensors and power consumption monitors, which are easy to deploy with less risk of privacy violation. It is significant to monitor daily life of elderly living alone to prevent them from having less exercise at home and from being at home with a depression tendency. Taking those sensor values as the input, our method utilizes Markov Logic Network (MLN) to express the association rules of sensors and activities as soft logical expressions. Then using the estimated activities from MLN and user feedbacks, the method trains MLN for better accuracy of recognition. We have implemented our system as a monitoring system that summarizes the activities of daily living as a timetable, which facilitates for remote family to monitor target elderly. We have deployed the system to two real houses of elderly people, and have shown the average accuracy was more than 85%.