1A1-L06 室内センサデータ蓄積による行動記述と異変検知アルゴリズム(人間機械協調)

概要

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In this paper, we propose two components, behavior description and anomaly detection algorithm in daily life. To begin with, action labels are assigned for each data-segment, using HMM (Hidden Markov Model) and k-means method. In traditional method, HMMs are composed for all data-segments. This paper improved our previous method, and succeeded to reduce calculation time. In anomaly detection step, typical action data are acquired, using probabilistic density of occurrence and successive time. Each probabilistic density is composed based on accumulating labeled-data, using SDLE (Sequential Discounting Laplace Estimation) and SDEM (Sequential Discounting Expectation and Maximization) algorithms. When new data come, if typical action data is changed largely, the data are detected anomaly.
一般社団法人日本機械学会の論文
2007-05-11