Recognition of Human Activities With Wearable Sensors
A novel approach for recognizing human activities with wearable sensors is investigated in this paper. The key techniques of this approach include the Generalized Discriminant Analysis (GDA) and the Relevance Vector Machines (RVM). The feature vectors extracted from the measured signal are processed by GDA, with its dimension remarkably reduced from 350 to 12 while fully maintaining the most discriminative information. The reduced feature vectors are then classified by the RVM technique according to an extended multiclass model, which shows good convergence characteristic. Experimental results on the Wearable Action Recognition Dataset demonstrate that the authors' approach achieves an encouraging recognition rate of 99.2%, true positive rate of 99.18% and false positive rate of 0.07%.