This paper proposes cooking support using ubiquitous sensors. We developed a machine learning algorithm that recognizes cooking procedures by taking into account widely varying sensor information and user behavior. To provide appropriate instructions to users, we developed a Markov-model-based behavior prediction algorithm. Using these algorithms, we developed cooking support automatically displaying cooking instruction videos based on user progress. Experiments and experimental results confirmed the feasibility of our proposed cooking support.
Home energy savings and low CO2 emissions are indispensable against global warming. This paper proposes a home energy conservation support system by using ubiquitous sensors. This system encourages energy conservation to the residents by indicating the non-energy conservation activities and the estimated loss money. This system is composed by human activities and environment monitoring system and energy-saving advice system. Human activities and environment monitoring system detects non-energy saving activities by using ubiquitous sensors. The energy-saving advice system instructs the user energy-saving activities, which he should do by taking account on his non-energy saving activities. We confirmed the efficiency of the proposed system by experiments.
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