Behavior extraction from multiple sensors information for human activity monitoring

A monitoring system based on multiple microelectromechanical systems (MEMS) has been developed to maintain human healthcare. Using such a MEMS based monitoring system, several kinds of numerical data from several types of sensors can be measured. Our goal is to develop a intelligent monitoring system with small size. In order to microminiaturize the monitoring system, we need to minimize the power consumption. Therefore, we have to keep our intelligent system simple. We propose a behavior estimation method which consists of a SVM and a fuzzy rule based system to estimate the subject´s behavior. Our proposed method consists of two steps of abstraction. First, action primitives are defined. A SVM based classification system is trained using sample numerical data of action primitives. Then, the SVM based system classifies a part of numerical data into one of action primitives. Therefore, whole numerical data are expressed by a sequence of action primitives. Next, a fuzzy rule which maps a sequence of actions onto a behavior is defined for each behavior. In the second-step abstraction, each action sequence is expressed as a behavior by using the defined fuzzy rules. From the results of the abstraction, we can estimate the subject´s state.