Tomomasa Sato

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This paper proposes a behavior prediction system for supporting our daily lives. The behaviors in daily-life are recorded in an environment with embedded sensors, and the prediction system learns the characteristic patterns that would be followed by the behaviors to be predicted. In this research, the authors applied a method of discovering time-series(More)
A one-room-type sensing system named ”Robotic Room 11” for recognizing and accumulating human behavior an ordinary life is constructed. I n order to recognize human behaviors, we propose an idea that human behaviors can be estimated from the human position, posture, the objects surrounding the human and tame. Based on this idea, multiple sensors are(More)
This paper proposes a high resolution sensor floor which can detect both humans and robots simultaneously. Each sensor floor unit is 500mm square and is equipped with 4,096 pressure switches distributed in a 64 ×64 array. A 2m by 2m sensor floor with 16 of these sensor floor units has been realized. Experiments with this sensor floor have determined(More)
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 (a)labeling algorithms based on switching model around a behavioral(More)