2P1-E09 部屋型日常計測環境内のセンサデータに基づく行動予測と行動シミュレーションによる評価

概要

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This paper describes action prediction system based on sensor data in human behavior measurement environment. The prediction is based on time-series association rule method. The method detects characteristic sequences from sensor event sequence and taught behavior to be predicted. Based on the detected sequences, our system predicts occurrence of the resident's behavior with prediction reliability before arbitrary period. The sensor events for the method are provided from sensor-embedded environments or a behavior simulator. The simulator generates virtual sensor data with input behavior sequence and sensor configuration. We evaluated the action prediction system for real sensor data in our Sensing Room and virtual sensor data. The experiments show feasibility of the action prediction on such behaviors as drinking a bottle of tea.
一般社団法人日本機械学会の論文