Person identification by integrating wearable sensors and tracking results from environmental sensors

To provide personal and location-dependent services in public spaces such as shopping malls, it is important to be able to estimate the positions and identities of people in the environment. Sensors in the environment reliably detect their current positions, but it is difficult to identify people using these sensors. On the other hand, signals from wearable sensors can be used to identify people correctly, but precise position estimation remains problematic. In this paper, we describe a novel method of integrating laser range finders (LRFs) in the environment and wearable inertial sensors. Time sequences of angular velocities estimated from both LRFs and wearable sensors are matched to identify people. Examples of tracking individuals in the environment that confirm the effectiveness of this method are shown.