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An Adaptive Dual-Window Step Detection Method for a Waist-Worn Inertial Navigation System

  • Yanshun Zhang (a1), Yunqiang Xiong (a1), Yixin Wang (a1), Chunyu Li (a1) and Zhanqing Wang (a2)...

Abstract

In waist-worn pedestrian navigation systems, the periodic vertical acceleration peak signal at body centre of gravity is widely used for detecting steps. Due to vibration and waist shaking interference, accelerometer output signals contain false peaks and thus reduce step detection accuracy. This paper analyses the relationship between periodic acceleration at pedestrian centre of gravity and walking stance during walking. An adaptive dual-window step detection method is proposed based on this analysis. The peak signal is detected by a dual-window and the window length is adjusted according to the change in step frequency. The adaptive dual window approach is shown to successfully suppress the effects of vibration and waist shaking, thereby improving the step detection accuracy. The effectiveness of this method is demonstrated through step detection experiments and pedestrian navigation positioning experiments respectively. The step detection error rate was found to be less than 0·15% in repeated experiments consisting of 345 steps, while the longer (about 1·3 km) pedestrian navigation experiments demonstrated typical positioning error was around 0·67% of the distance travelled.

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References

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Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2011). Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation. The Journal of Navigation, 64, 219233.
Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2012). Using Constraints for Shoe Mounted Indoor Pedestrian Navigation. The Journal of Navigation, 65, 1528.
Cho, S.Y. and Park, C.G. (2006). MEMS Based Pedestrian Navigation System. The Journal of Navigation, 59, 135153.
Gusenbauer, D., Isert, C. and Krösche, J. (2010). Self-Contained Indoor Positioning on Off-The-Shelf Mobile Devices. Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1–9.
Jiménez, A.R., Seco, F., Prieto, C. and Guevara, J. (2009). A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU. Proceedings of the 6th IEEE International Symposium on Intelligent Signal Processing, 37–42.
Jiménez, A.R., Seco, F., Prieto, J.C. and Guevara, J. (2010). Indoor Pedestrian Navigation using an INS/EKF framework for Yaw Drift Reduction and a Foot-mounted IMU. Proceedings of the 7th Workshop on Positioning Navigation and Communication (WPNC), 135–143.
Jiménez, A.R., Seco, F., Zampella, F., Prieto, J.C. and Guevara, J. (2011). PDR with a Foot-Mounted IMU and Ramp Detection. Sensors, 11, 93939410.
Jiménez, A.R., Granja, F.S., Prieto Honorato, J.C. and Guevara Rosas, J.I. (2012). Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements. IEEE Transactions on Instrumentation and measurement, 61(1), 178189.
Jirawimut, R., Ptasinski, P., Garaj, V., Cecelja, F. and Balachandran, W. (2001). A Method for Dead Reckoning Parameter Correction in Pedestrian Navigation System. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference, 1554–1558.
Jahn, J., Batzer, U., Seitz, J., Patino-Studencka, L. and Boronat, J.G. (2010). Comparison and Evaluation of Acceleration Based Step Length Estimators for Handheld Devices. Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1–6.
Kappi, J., Syrjarinne, J. and Saarinen, J. (2001). MEMS-IMU Based Pedestrian Navigator for Handheld Devices. Proceedings of the 14th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2001), 1369–1373.
Lan, K. and Shih, W. (2012). Using Simple Harmonic Motion to Estimate Walking Distance for Waist-mounted PDR. 2012 IEEE Wireless Communications and Networking Conference: Mobile and Wireless Networks, 24452450.
Leppakoski, H., Kappi, J., Syrjarinne, J. and Takala, J. (2002). Error Analysis of Step Length Estimation Pedestrian Dead Reckoning. Proceedings of the 15th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2001), 1136–1142.
Levi, R.W. and Judd, T. (1996). Dead Reckoning Navigational System Using Accelerometer to Measure Foot Impacts. United States Patent, No.5, 583,776.
Lee, S.W. and Mase, K. (2001). Recognition of Walking Behaviors for Pedestrian Navigation. Proceedings of the 2001 IEEE International Conference on Control Applications, 1152–1155.
Ladetto, Q. (2000). On Foot Navigation: Continuous Step Calibration Using Both Complementary Recursive Prediction and Adaptive Kalman Filtering. Proceedings of the 13th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2000), 1735–1740.
Ryu, U., Ahn, K., Kim, E., Kim, M., Kim, B., Woo, S. and Chang, Y. (2013). Adaptive Step Detection Algorithm for Wireless Smart Step Counter. Information Science and Application (ICISA), 1–4.
Schindhelm, C.K. (2012). Activity recognition and step detection with smartphones: Towards terminal based indoor positioning system. Proceedings of 2012 IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 24542459.
Shin, S.H., Lee, M.S. and Park, C.G. (2010). Pedestrian Dead Reckoning System with Phone Location Awareness Algorithm. Position Location and Navigation Symposium (PLANS), IEEE/ION, 97101.
Shin, S.H., Park, C.G., Kim, J.W. and Lee, J.M. (2007). Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors. Sensors Applications Symposium (SAS), 15.
Sun, Z., Mao, X., Tian, W. and Zhang, X. (2009). Activity classification and Dead Reckoning for Pedestrian Navigation with Wearable Sensors. Measurement Science and Technology, 20(1), 110.
Susi, M., Renaudin, V. and Lachpelle, G. (2013). Motion mode recognition and step detection algorithms for mobile phone users. Sensors, 13(2), 15391562.
Tumkur, K. and Subbiah, S. (2012). Modelling Human Walking for Step Detection and Stride Determination by 3-Axis Accelerometer Reading in Pedometer. Proceedings of the 4th International Conference on Computational Intelligence, Modeling and Simulation, 199204.
Weinberg, H. (2002). Using the ADXL202 in Pedometer and Personal Navigation Applications. http://www.analog.com/media/en/technical-documentation/application-notes/513772624AN602.pdf. Accessed 2002.

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