Pedestrian Identification by Associating Walking Rhythms from Wearable Acceleration Sensors and Biped Tracking Results

Providing personal and location-dependent services is one of the promising services in public spaces like a shopping mall. So far, sensors in the environment have reliably detected the current positions of humans, but it is difficult to identify people using these sensors. On the other hand, wearable devices can send their personal identity information, but precise position estimation remains problematic. In this paper, we propose a novel method of integrating laser range finders (LRFs) in the environment and wearable accelerometers. The legs of pedestrians in the environment are tracked by using LRFs, and acceleration signals from pedestrians are simultaneously observed. Since the tracking results of biped feet and the body oscillation of the same pedestrian show same walking rhythm patterns, we associate these signals from same pedestrian that maximizes correlation between them and identify the pedestrian. Example results of tracking individuals in the environment confirm the effectiveness of this method.