Extraction of human behavior from human activity monitoring data using MEMS sensors
Manabu Nii, Kazuki Nakai, Yutaka Takahashi · 2011
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.