Human behavioral detection and data cleaning in assisted living environment using wireless sensor networks

Abstract

Due to the increasing number of the elderly, more and more people need to have additional health care such as medical or environmental monitoring information at home or nursing facility. Most elderly people are likely to have a sudden behavioral changes due to their aging or existing health problems. Therefore, it is necessary to have an autonomous system that can monitor them in order to prevent emergent situation in advance. In this paper, we present a wireless sensor network system that can recognize human behavioral patterns of the elderly who lives alone. We model episodes that are series of events for a person who lives in an one-bedroom apartment. We propose data cleaning techniques in both sensor and base station sides for the erroneous environment of wireless sensor networks. Based on these techniques, we try to extract discrete events as close as possible to effective events. We introduce non-real time analysis to recognize human behavioral patterns on the centralized system, which can be further extended to a real-time analysis. We also adopt an existing search technique to apply it to detect similar or abnormal behavior. We experiment the proposed system by gathering behavioral pattern data from the miniature one-bedroom apartment that is equipped with SunSPOTs in our HERACLEIA Laboratory. We look up the resulting episodes from our experiment in the dictionary that is a set of predetermined episodes using the suggested algorithm.