Abstract
To date, there have been numerous studies focusing on how user’s activity can be identified and predicted without considering motivation driving an action. However, understanding the underlying motivation is a key to activity analysis. On the other hand, user’s desires often generate motivations to engage activities in order to fulfill such desires. Thus, we must study user’s desires in order to provide proper services to make the life of users more comfortable.
In this paper, we describe what possible factors are relevant to determining user’s desire. To achieve this, a full-scale experiment has been conducted. Raw data from sensors were interpreted as context information. We observed the user’s activities and get user’s emotions as a part of inference parameters. Throughout the experiment, a complete analysis was conducted whereas 30 factors were considered and most relevant factors were selectively chosen using correlation coefficient and delta value. Our preliminary results show that 11 factors (3 emotions, 7 behaviors and 1 location) are relevant to inferring user’s desire. By applying such factors into Dynamic Bayesian Network to infer desire, both complexity and scalability as our biggest research challenges can be more adequately tackled.
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Dong, J., Yang, HI., Chang, C.K. (2013). Identifying Factors for Human Desire Inference in Smart Home Environments. In: Biswas, J., Kobayashi, H., Wong, L., Abdulrazak, B., Mokhtari, M. (eds) Inclusive Society: Health and Wellbeing in the Community, and Care at Home. ICOST 2013. Lecture Notes in Computer Science, vol 7910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39470-6_29
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DOI: https://doi.org/10.1007/978-3-642-39470-6_29
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