Action Estimation from Human Activity Monitoring Data Using Soft Computing Approach

In order to maintain human health care, it is important to record daily activity. For recording daily human activity, monitoring system which consists of multiple micro electromechanical systems (MEMS) has been developed. Using the MEMS based monitoring system, numerical data of subject's activity can be stored into a database. For example, when subject's activity on a single day is recorded, a huge volume of data is saved. To estimate the subject's activity condition from such a huge volume data, a fuzzy rule based approach is used in our study. Our proposed method consists of two steps of abstraction. First, action primitives are defined. In the first-step abstraction, sensor data is expressed as a sequence of actions by using the defined action primitives. Next, a fuzzy rule which maps a sequence of actions to a behavior is defined for each behavior. In the second-step abstraction, each sequence of actions is expressed as a behavior. From the results of abstraction, we can estimate the subject's state.