1A1-D12 Behavior Labeling based on Change Point Detection of Sensor Data in Room Environment

Abstract

This paper presents a behavior labeling algorithm for time-series data, extracted using home sensors. In the previous work, we proposed a time-series data clustering method based on Hidden Markov Models (HMMs). However, change points of behaviors are not clearly segmented in our former method and short length behaviors tend to be misrecognized. In this paper, we propose a novel behavior labeling algorithm by introducing Singular Spectrum Transformation (SST) to detect change-points robustly. This algorithm enables more precise behavior labeling because of clear change-point detection.