Enhancing activity recognition using CPD-based activity segmentation

Abstract Segmenting behavior-based sensor data and recognizing the activity that the data represents are vital steps in all applications of human activity learning such as health monitoring, security, and intervention. In this paper, we enhance activity recognition by identifying activity transitions. To accomplish this goal, we introduce a change point detection-based activity segmentation model which partitions behavior-driven sensor data into non-overlapping activities in real time. In addition to providing valuable activity information, activity segmentation also can be used to improve the performance of activity recognition. We evaluate our proposed segmentation-enhanced activity recognition method on data collected from 29 smart homes. Results of this analysis indicate that the method not only provides useful information about activity boundaries and transitions between activities but also increases recognition accuracy by 7.59% and f measure by 6.69% in comparison with the traditional window-based methods.

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