Pattern recognition of gait signals using φ-OTDR

Gait recognition is of great significance in many fields such as human identity recognition, medical rehabilitation, remote monitoring, and so on. In this paper, the gait signal pattern recognition as well as the identity recognition is achieved through machine learning based on the data obtained by phase sensitive time-domain reflectometer (phi-OTDR). The dynamic phase variation caused by the pedaling directly acting on the fiber was recorded. By extracting the temporal-spatial features of the recorded signal, the recognition accuracy of the two experimenters can be resolved as high as 90% via a convolutional neural network (CNN). It provides an effective solution for human identity identification in perimeter security applications using phi-OTDR.