Everyday Life Behavior Modeling Based on Spatio-Temporal Expansion of Behavior Metrics Sensing Using Location-EMG Sensor

Understanding everyday life behavior requires multidisciplinary and integrated research. This stems from the fact that everyday life behavior is much higher dimensional phenomena than a single academic society can deal with. This paper discusses a method for representing and processing information to deal with everyday life behavior and proposes a new concept of a “spatio-temporal-semantic mapping system (STS Map)” that allows us to standardize and integrate a variety of information and to share information multidisciplinary. The features of the STS Map lie in 1) spatio-temporal expansion of behavior metrics sensing with location sensor, 2) representation based on an environmental coordinate system, 3) a statistical modeling process for knowledge acquisition and a retargeting process. To show the effectiveness of the proposed STS mapping system, this paper reported an implemented system and a case study using the implemented system. In the case study, we conducted in situ measurement of 47 children playing with equipment by a wireless wearable location-electromyography sensor and created two models on a children's skill from the measured data. This paper reported cross-validation of the constructed behavior models and compared them. This paper also reported a new design of play equipment having a climbing part that was suitable for the target age group of children. The new design was conducted using the created model in collaboration with playground equipment designers.