Introducing knowledge in the process of supervised classification of activities of Daily Living in Health Smart Homes

Abstract

Telemedicine and Telemonitoring of elderly people is an actual challenge that is explored to prevent some problems linked to the constant growing of the mean age of the population. It requires to recognize the behavior and the actions of a person inside his own home with non-intrusive sensors and to process data to check the evolution of the person. Activities of Daily Living can be learned and automatically recognized using supervised classification on sensor data. This paper presents the results of the study of prior introduction, in Support Vector Machine, to improve this automatic recognition of Activities of Daily Living. We started from a set of data acquired in daily life during an experimentation in the Health Smart Home of the TIMC-IMAG Lab. From this restricted set of data, we obtained models for seven activities of Daily Living and test, with leave-one-out method, the performance of this classification. This first step gave baseline results that this paper tends to improve using consistent priors to compute more specific and accurate models of the different activities that are learned and obtain better results on the leave-one-out method on the sensors data.