Centralized class specific dictionary learning for wearable sensors based physical activity recognition

With recent progress in pervasive healthcare, physical activity recognition with wearable body sensors has become an important and challenging area in both research and industrial communities. Here, we address a novel technique for a sensor platform that performs physical activity recognition by leveraging a class specific regularizer term into the dictionary pair learning objective function. The proposed algorithm 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. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby proving beneficial for the classification stage. In order to develop a more practical approach, we employ a combination of an alternating direction method of multipliers and a l
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minimization method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on two activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities. Experimental results demonstrate that classifiers built in this dictionary learning based framework outperforms state of art algorithms by using simple features, thereby achieving competitive results when compared with classical systems built upon features with prior knowledge.