A VERIFICATION APPROACH FOR HUMAN BEHAVIOR MODELING
DOI:
https://doi.org/10.23055/ijietap.2018.25.1.3777Keywords:
behavior mining, daily common behavior, sequential rules miningAbstract
Human behavior computing has received a lot of attention in many business domains to derive daily routines, such as company’s regular operations and cultural activities. Using the recent technology such as adaptive sensor devices, human movement and activity can be detected and traced. Discovering daily routines from lifelog can assist the growth of individual and organizations but yet needs reliable approach to produce a representative behavior model due to practicality issues. This study presents norm approaches to assess the deviation between model space and daily self-elicited behavior (lifelog). Two approaches, approximate and aggregate norm, are proposed to evaluate the discovered (routine) model according to the respective lifelog and a particular person. In this study, the model has been derived using the concept of time lapse embedded in the sequential rules. The result of the experiment using the two norm approaches show that the routine models have 99% characteristics compared with the lifelog.
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