A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer

Human activity recognition using wearable body sensors plays a vital role in the field of pervasive computing. In this paper, we present human activity recognition framework using compressive classification of data collected from a tri-axial accelerometer sensor. Inspired by the theories of random projection, we propose a novel chaotic map for dimensionality reduction of the accelerometer raw data. This framework also involves extraction of time and frequency domain features from the compressed data. These features are used for human activity recognition using a sparse based classifier. Thus, a simultaneous dimension reduction and classification approach is presented in this paper. We experimentally validate the effectiveness of our proposed framework by recognizing 8 common daily human activities performed by 15 subjects of varying age groups. Our proposed framework achieves superior performance in terms of specificity, precision, F-score and overall accuracy.