Behavior labeling algorithms from accumulated sensor data matched to usage of livelihood support application

This paper presents three behavior labeling algorithms based on supervised learning using accumulated pyroelectric sensor data in the living space. We summarize features of each algorithm to use them in combination matched to usage of the livelihood support application. They are (1) labeling algorithms based on time attribution of ldquoon-offrdquo data, (2) one based on Hidden Markov Models, and (3) one based on switching model around a behavioral change-point. We show the behavior labeling results of three algorithms for one month data under the same conditions. Then we point out features on the basis of these results.