Rank Preserving Discriminant Analysis for Human Behavior Recognition on Wireless Sensor Networks

With the rapid development of the intelligent sensing and the prompt growing industrial safety demands, human behavior recognition has received a great deal of attentions in industrial informatics. To deploy an utmost scalable, flexible, and robust human behavior recognition system, we need both innovative sensing electronics and suitable intelligence algorithms. Wireless sensor networks (WSNs) open a novel way for human behavior recognition, because the heavy computation can be immediately transferred to a network server. In this paper, a new scheme for human behavior recognition on WSNs is proposed, which transmits activities´ signals compressed by Hamming compressed sensing to the network server and conducts behavior recognition through a collaboration between a new dimension reduction algorithm termed rank preserving discriminant analysis (RPDA) and a nearest neighbor classifier. RPDA encodes local rank information of within-class samples and discriminative information of the between-class under the framework of Patch Alignment Framework. Experiments are conducted on the SCUT Naturalistic 3D Acceleration-based Activity (SCUT NAA) dataset and demonstrate the effectiveness of RPDA for human behavior recognition.