Adding Structural Characteristics to Distribution-Based Accelerometer Representations for Activity Recognition Using Wearables

Feature extraction is a critical step in sliding-window based standard activity recognition chains. Recently, distribution based features have been introduced that showed excellent generalization capabilities across a wide range of application domains in human activity recognition scenarios based on body-worn sensors. These features capture the data distribution of individual analysis frames, yet they ignore temporal structure inherent to the signal of a frame. We explore four variants of adding temporal structure to distribution based features and demonstrate their potential for statistically significant improvements of activity recognition in general. The addition of temporal structure comes with a moderate increase in computational complexity rendering the proposed methods applicable to mobile and embedded scenarios.