Extraction of human behavior from human activity monitoring data using MEMS sensors

To record daily activity for well-maintained human health care, a monitoring system based on multiple microelectromechanical systems (MEMS) has been developed. Several kinds of numerical data of subject´s activity can be stored using the MEMS based monitoring system. When we use subject´s activity on a single day, a huge volume of data is obtained and recorded. In order to estimate the subject´s behavior from such a huge volume data, we propose a behavior estimation method which consists of a SVM and a fuzzy rule based system. Our proposed method consists of two steps of abstraction. First, action primitives are defined and a SVM based classification system is generated from sample numerical data of action primitives or human knowledge. Then, the SVM classifies a part of numerical data into each action primitive. Therefore, 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 by human user 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.