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WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors

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Abstract

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 <2 m, 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.

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Acknowledgments

This work was partially supported by the National Key Basic Research Program of China “973 Project” (Grant No. 2011CB707106), the National High-Tech Research and Development Program of China “863 Project” (Grant No. 2013AA122301), the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1278), the National Natural Science Foundation of China NSFC (Grant No.F020201, 61070013, 61250110541, U1135005, 61303212), “Hundred Talents Recruitment Program” of Global Experts of Hubei, and the US National Science Foundation (Grant No. CNS-1136027).

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Authors and Affiliations

  1. Computer School, Wuhan University, Wuhan, China

    Xiaoguang Niu, Meng Li, Jin Liu & Shubo Liu

  2. International School of Software, Wuhan University, Wuhan, China

    Xiaohui Cui

  3. State Key Lab of Software Engineering, Wuhan University, Wuhan, China

    Jin Liu

  4. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

    Kaushik R. Chowdhury

Authors
  1. Xiaoguang Niu
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  2. Meng Li
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  3. Xiaohui Cui
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  4. Jin Liu
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  5. Shubo Liu
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  6. Kaushik R. Chowdhury
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Corresponding authors

Correspondence to Xiaohui Cui or Jin Liu.

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Niu, X., Li, M., Cui, X. et al. WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors. Pers Ubiquit Comput 18, 1901–1915 (2014). https://doi.org/10.1007/s00779-014-0796-x

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  • DOI: https://doi.org/10.1007/s00779-014-0796-x

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