Human motion analysis using expressions of non-separated accelerometer values as character strings

People generally perform various activities, such as walking and running. They perform these activities with different motions. For example, walking can be performed with or without swinging shoulders, as well as staggering and swinging arms. We assume that such differences occur based on physical and mental characteristics of humans. To analyze relations between the motions and the characteristics/conditions, it is useful to group humans according to these differences. In a previous work, we proposed a method that successfully grouped humans by analyzing accelerometer data of their bodies in a specific activity with fixed timing and duration. In this study, we tackle with a problem of grouping human in generic, variable-length activities, such as walking and running. We propose a method that detects same motions from the accelerometer data with sliding windows and merges continuous same motions into a motion. The method is robust regarding the difference in timing and duration of the motion. In our conducted experiments, the proposed method classified humans into groups appropriately, the groups which are acquired by the previous method with the same data but without assuming fixed timing and fixed duration, which are assumed in the previous method. The proposed method is robust against temporally noised data generated from the data.