WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors
Indoor tracking systems have become very popular, wherein pedestrian movement is analyzed in a variety of commercial and secure spaces. The inertial sensor-based method makes great contributions to continuous and seamless indoor pedestrian tracking. However, such a system is vulnerable to the cumulative locating errors when moving distance increases. Inaccurate heading values caused by the interference of body swing of natural walking and the geomagnetic disturbances are the main sources of the accumulative errors. To reduce such errors, additional infrastructure or highly accurate sensors have been used by previous works that considerably raise the complexity of the architecture. This paper presents an indoor pedestrian tracking system called WTrack, using only geomagnetic sensors and acceleration sensors that are commonly carried by smartphones. A fine-grained walk pattern of indoor pedestrians is modeled through Hidden Markov Model. With this model, WTrack can track indoor pedestrians by continuously recognizing the pre-defined pedestrians walk pattern. More importantly, WTrack is able to resist both the interference of body swing of natural walking and the geomagnetic disturbances of nearby objects. Our experimental results reveal that the location error is <2m, which is considered adequate for indoor location-based-service applications. The adaptive sample rate adjustment mode further reduces the energy consumption by 52% in comparison, as opposed to the constant sampling mode.
walk pattern recognition indoor pedestrian tracking smartphone inertial sensing ubiquitous computing