Rank Preserving Discriminant Analysis for Human Behaviour Recognition Using Dash7 Protocol
With the rapid development of the intelligent sensing and the prompt growing industrial safety demands, human behavior recognition has received a lot 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. In this paper, a new scheme for human behavior recognition on wireless sensor networks is proposed, namely Rank preserving discriminant analysis algorithm, which transmits the activities recognized from human or subject to network server. This activity signals are compressed by Hamming Compressed Sensing and the compressed signals are send to the network server. Heavy computations are performed by the network server and decompression also performed by network server. In the network server the classification process will takes place using nearest neighborhood algorithm. For transmitting of activity signals we are using Dash7 protocol which covers large area. Finally the results will be returned to the server. RPDA encodes local rank information of within-class samples and discriminative information of the betweenclass 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.