Physical and Behavioral Characterization of Human Groups Classified Using Symbolic Pattern Analysis

In daily life we perform various activities, such as walking and running. Even if we take an movement for a purpose, behaviors in the movement may differ according to physical/mental condition, background culture of the persons or other factors. If a device, such as a smart phone and a wearable device, can know the condition by observing behaviors, it may be able to use such information for appropriate service. In a previous research we proposed an algorithm which groups humans based on co-occurrence and exclusiveness of patterns between behaviors in Radio Gymnastic Exercises, which is a convenient benchmark for our purpose. In this work we focus on relation between human groups and physical/behavioral property of humans. After we revise the algorithm by introducing similarity among patterns, we characterize groups derived by the modified algorithm using various information by questionnaire and video data of the exercises. In our analysis we confirmed that the groups were characterized better by physical conditions than observed behaviors.