Home-based activity monitoring of elderly people through a hierarchical approach

Abstract

People that need assistance, as for instance elderly or disabled people, may be affected by a decline in daily functioning that usually involves the reduction and discontinuity in daily routines and a worsening in the overall quality of life. Thus, there is the need to intelligent systems able to monitor indoor and outdoor activities of users to detect emergencies, recognize activities, send notifications, and provide a summary of all the relevant information. To this end, several sensorbased telemonitoring and home support systems have been presented in the literature. Unfortunately, performance of those systems depends, among other characteristics, on the reliability of the adopted sensors. Although binary sensors are quite used in the literature and also in commercial solutions to identify user’s activities, they are prone to noise and errors. In this chapter, we present a hierarchical approach, based on machine learning techniques, aimed at reducing errors from the sensors. The proposed approach is aimed at improving the classification accuracy in detecting if a user is at home, away, alone or with some visits. It has been integrated in a sensor-based telemonitoring and home support system. After being evaluated with a control user, the overall system has been installed in 8 elderly people’s homes in Barcelona, results are presented in this chapter.