Distance measurement using proximity sensor in pedestrian and bicycle navigation

  • Authors

    • Mohamed shebl
    • Mohamed El-Tokhey
    • Tamer Fathy
    • Yasser Mogahed
    • Mohamed El-Habiby
    2018-12-29
    https://doi.org/10.14419/ijet.v7i4.15999
  • Dead Reckoning Navigation, Step Counting, Cycle Counting, Proximity Sensor, PDR and CDR.

  • Abstract

    Navigation plays an important role in life where it has applications in many disciplines such as transportation services and infrastructure maintenance. Navigation applications depend on reliable, trustful and continuous navigation solution that overcomes the Global Navigation Satellite System (GNSS) denied environments. To achieve this issue, GNSS is now commonly used with other navigation systems such as Inertial Navigation System (INS). Pedestrian dead reckoning usually uses the resultant of 3-axes accelerometer values for step detection; the randomness of the pedestrian hand-held habit the step detection cannot always be accurate by using the accelerometer sensor data. Furthermore, the signal trend of the accelerometer could differ significantly due to the carrying modes and the user’s hand high dynamics.

    This paper represents a new idea for distance measurement during dead reckoning navigation depending on the proximity sensor which attached to most smartphones to lock screen while making calls for power saving. The new idea summarized in using the proximity sensor for steps counting in case of pedestrian dead reckoning (PDR) or cycles counting in case of bicycle dead reckoning (CDR).

    In PDR the distance measurement is depending on counting number of steps and knowing the stride length of user which is variable from person to other, so a least square models have been constructed to estimate the stride length of person depending on his height, weight ,age and gender.

    Although stride length estimation for users but the PDR has a less accuracy comparing with CDR where the stride length can be changed while walking and cannot be controlled like cycle length.

    The experiments proved that the proximity sensor is a good tool in detecting number of steps and cycles in PDR and CDR respectively, the bicycle mode gives more accurate results in distance measurements compared with walking mode where the average error in case of PDR= 2 % and in case of CDR=0.53 % .

     

     


  • References

    1. [1] Hwan-hee Lee , Suji Choi and Myeong-jin Lee (2015).“Step Detection Robust against the Dynamics of Smartphones“.licensee MDPI, Basel, Switzerland.

      [2] Zengshan Tian, Yuan Zhang, Mu Zhou and Yu Liu (2014).“Pedestrian dead reckoning for MARG navigationusing a smartphone“.EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1186/1687-6180-2014-65.

      [3] Yu Liu, Yanping Chen, Lili Shi, Zengshan Tian, Mu Zhou, and Lingxia Li (2015). “Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices“. Journal of Communications Vol. 10, No. 7, July 2015. https://doi.org/10.12720/jcm.10.7.520-525.

      [4] Najme Zehra, Ashwani Kumar, Aanchal Chauhan,and Kritka Sahni (2012).“Step Counting Using Smartphone-Based Accelerometer“. International Journal on Computer Science and Engineering (IJCSE) Vol. 4 No. 05 May 2012.

      [5] Lin, J., Chan., L., Yan, H., (2015) “A Decision Tree Based Pedometer and Its Implementation on the Android Platform“. Third International Conference on Signal, Image Processing and Pattern Recognition (SIPP 2015).

      [6] Seo, J., Chiang, Y., Laine, T., H. and Khan. A. M., (2015). “Step counting on smartphones using advanced zero-crossing and linear regression“. In Proceedings of the ninth International Conference on Ubiquitous Information Management and Communication (IMCOM '15). ACM, New York, NY, USA, Article 106. https://doi.org/10.1145/2701126.2701223.

      [7] Vyas, N., Farringdon, J., Andre, D., Stivoric, J., (2011) “Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure“. Proceedings of the Twenty-Third Innovative Applications of Artificial Intelligence Conference, 2011.

      [8] Brajdic, A., and Harle, R., (2013) “Walk Detection and Step Counting on Unconstrained Smartphones“. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp '13).

      [9] Khedr, M., and El-Sheimy, N., (2017) “A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applicationsâ€. Sensors 2017, 17(11), 2573. https://doi.org/10.3390/s17112573.

      [10] Kang, X., Huang, B., and Qi, G., (2018) “A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphonesâ€. Sensors 2018, 18, 297. https://doi.org/10.3390/s18010297.

      [11] Valérie Renaudin, Melania Susi and Gérard Lachapelle (2012). “Step Length Estimation Using Handheld Inertial Sensors“.licensee MDPI, Basel, Switzerland.

  • Downloads

  • How to Cite

    shebl, M., El-Tokhey, M., Fathy, T., Mogahed, Y., & El-Habiby, M. (2018). Distance measurement using proximity sensor in pedestrian and bicycle navigation. International Journal of Engineering & Technology, 7(4), 4266-4270. https://doi.org/10.14419/ijet.v7i4.15999