User Identification Using User's Walking Pattern over the ubiFloorII

In this paper, we propose ubiFloorII, a novel floor-based user identification system to recognize humans based on their walking pattern such as stride length, dynamic range, foot angle, and stance and swing time. To obtain users walking pattern from their gait, we deployed photo interrupter sensors instead of switch sensors used in ubiFloorI. We developed a software module to extract walking pattern from users’ gait. For user identification, we employed neural network trained with users’ walking samples. We achieved about 96% recognition accuracy using this floor-based approach. The ubiFloorII system may be used to automatically and transparently identify users in home-like environments.