Correlation Analysis-Based Classification of Human Activity Time Series

Segmentation of sequential sensor data streams and classification of each segment are common steps in tasks dealing with the detection of events of interest in such data. In this paper, we introduce two correlation analysis-based methods for classifying time series data generated by sensors. Our first method is a lightweight supervised approach utilizing principal component analysis to jointly segment data and classify each segment into a class corresponding to an event of interest. The second method relies on unsupervised canonical correlation analysis to segment time series by clustering together consecutive data points that belong to the same event. Both methods operate without the need for prior feature extraction from the data. The theoretical model of the two methods and the solution to the resulting optimization problem are presented in detail. Classification of human activity from inertial measurement unit sensor data is used as a case study to demonstrate the applicability and effectiveness of the proposed methods.