Maximum Correntropy Based Dictionary Learning Framework for Physical Activity Recognition Using Wearable Sensors

Abstract

Due to its symbolic role in ubiquitous health monitoring, physical activity recognition with wearable body sensors has been in the limelight in both research and industrial communities. Physical activity recognition is difficult due to the inherent complexity involved with different walking styles and human body movements. Thus we present a correntropy induced dictionary pair learning framework to achieve this recognition. Our algorithm for this framework jointly learns a synthesis dictionary and an analysis dictionary in order to simultaneously perform signal representation and classification once the time-domain features have been extracted. In particular, the dictionary pair learning algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we employ a combination of alternating direction method of multipliers and an iteratively reweighted method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on an activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities from the recently released PAMAP2 dataset. Experimental results indicate that classifiers built using this correntropy induced dictionary learning based framework achieve high accuracy by using simple features, and that this approach gives results competitive with classical systems built upon features with prior knowledge.