Gait-based Human Identification Using Deep Learning and Multiple-position Wearable Data

Gait-based human identification (GHID) is a promising biometric technology that recognizes individuals based on their unique walking patterns. This paper explores the contribution of each single body position and the data fusion of multiple positions to GHID. We develop two models, namely temporal network (TNET) and spatial network (SNET), for extract temporal and spatial features in the GHID task. The experimental results on our collected multiple-body wearable data demonstrate that the Right Hip position achieves the highest accuracy of 96.45% in the single-position setting using TNET. Subsequently, the fusion of data from Right Hip with other body positions is explored. The combination of Right Hip and Right Elbow performs the best, achieving the two-position fusion setting with an accuracy of 99.11% based on TNET.