AN AN APPROACH TO HUMAN ACTIVITY CLUSTERING USING INERTIAL MEASUREMENT DATA
Automatic human activity recognition is regarded as an important task in security, military and police applications. This paper reports on a pilot study of an approach to human activity clustering using inertial measurement data. At the signal level, we particularly consider the angular velocity and instantaneous acceleration data obtained from a three-axis inertial measurement unit placed on the right arm of the human subject. At the methodology level, the approach consists of three components: symbol-based modeling of spatiotemporal signals, an adaptation of the Levenshtein distance, and a graph-based clustering algorithm. A prototype system implementing the proposed approach is evaluated on recordings of six human subjects involved in four task-oriented activities with significant intercluster similarity. The clustering results are assessed using the Rand index (RI=0.921), the precision rate (P=1), the recall rate (R=0.636), and the balanced F-measure (F=0.778).
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