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Detecting Turns and Correcting Headings Using Low-Cost INS

Published online by Cambridge University Press:  27 July 2017

Mohd Nazrin Muhammad Open the ORCID record for Mohd Nazrin Muhammad [Opens in a new window] ,
Zoran Salcic  and
Kevin I-Kai Wang
Mohd Nazrin Muhammad*
Affiliation:
(Department of Electrical & Computer Engineering, University of Auckland, New Zealand) (Department of Robotics & Automation, Universiti Teknikal Malaysia Melaka, Malaysia)
Zoran Salcic
Affiliation:
(Department of Electrical & Computer Engineering, University of Auckland, New Zealand)
Kevin I-Kai Wang
Affiliation:
(Department of Electrical & Computer Engineering, University of Auckland, New Zealand)
*
(E-mail: mmuh542@aucklanduni.ac.nz)
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Abstract

Unlike industrial-grade Inertial Navigation Sensors (INSs) that can provide credible tracking performance, more affordable consumer-grade low-cost INSs produce drifts in heading angles and positions that result in a poor tracking accuracy. Researchers have proposed drift correction methods that attempt to attenuate the drifts when walking straight along the dominant directions is detected. While determining the type of a pedestrian's walk is essential before the heading corrections are made, the current detection techniques heavily rely on thresholding. This paper proposes a novel threshold-less method to detect turns in walking by using pelvic rotation and correct the heading angle based on consumer-grade INSs. The experiments indicate the proposed turn detector and heading correction methods produce very good results which can be applied for future pedestrian tracking, activity recognition or rehabilitation.

Keywords

Heading correctionPelvic rotationTurn detectionZero-velocity update
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 189 - 207
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2011). Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation. Journal of Navigation, 64 (2), 219–233.Google Scholar
Akeila, E., Salcic, Z. and Swain, A. (2014). Reducing Low-Cost INS Error Accumulation in Distance Estimation Using Self-Resetting. IEEE Transactions on Instrumentation and Measurement, 63 (1), 177–184.Google Scholar
Borenstein, J. and Ojeda, L. (2010). Heuristic Drift Elimination for Personnel Tracking Systems. Journal of Navigation, 63 (4), 591–606.CrossRefGoogle Scholar
Borhani, M., McGregor, A.H. and Bull, A.M.J. (2013). An Alternative Technical Marker Set for The Pelvis Is More Repeatable Than the Standard Pelvic Marker Set. Gait and Posture, 38 (4), 1032–1037.Google Scholar
Bruijn, S.M., Meijer, O.G., Van Dieen, J.H., Kingma, I. and Lamoth, C.J. (2008). Coordination of Leg Swing, Thorax Rotations, and Pelvis Rotations During Gait: The Organisation of Total Body Angular Momentum. Gait & Posture, 27 (3), 455–462.Google Scholar
Bugane, F., Benedetti, M.G., D'Angeli, V. and Leardini, A. (2014). Estimation of Pelvis Kinematics in Level Walking Based on A Single Inertial Sensor Positioned Close to The Sacrum: Validation on Healthy Subjects with Stereophotogrammetric System. Biomedical Engineering Online, 13 (1), 1.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for The Behavioral Sciences. Hillsdale, N.J., L. Erlbaum Associates.Google Scholar
Floor-Westerdijk, M.J., Schepers, H.M., Veltink, P.H., van Asseldonk, E.H. and Buurke, J.H. (2012). Use of Inertial Sensors for Ambulatory Assessment of Center-of-Mass Displacements During Walking. IEEE Transactions on Biomedical Engineering, 59 (7), 2080–2084.Google Scholar
Gu, Y., Song, Q., Li, Y. and Ma, M. (2014). Foot-mounted Pedestrian Navigation based on Particle Filter with an Adaptive Weight Updating Strategy. Journal of Navigation, 68 (1), 23–38.Google Scholar
Ilyas, M., Cho, K., Baeg, S.-H. and Park, S. (2016). Drift Reduction in Pedestrian Navigation System by Exploiting Motion Constraints and Magnetic Field. Sensors, 16 (9), 1455.Google Scholar
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 (10), 9393–9410.Google Scholar
Ju, H.J., Lee, M.S., Park, C.G., Lee, S. and Park, S., (2014). Advanced Heuristic Drift Elimination for Indoor Pedestrian Navigation. 2014 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, 729–732.Google Scholar
Kim, B. and Kong, S. (2016). A Novel Indoor Positioning Technique Using Magnetic Fingerprint Difference. IEEE Transactions on Instrumentation and Measurement, 65 (9), 2035–2045.Google Scholar
Kuo, A.D. (2007). The Six Determinants of Gait and The Inverted Pendulum Analogy: A Dynamic Walking Perspective. Human Movement Science, 26 (4), 617–656.Google Scholar
Liang, B.W., Wu, H.W., Meijer, O.G., Lin, J.H., Lv, G.R., Lin, X.C., Prins, M.R., Hu, H., Van Dieën, J.H. and Bruijn, S.M. (2014). Pelvic Step: The Contribution of Horizontal Pelvis Rotation to Step Length in Young Healthy Adults Walking on A Treadmill. Gait and Posture, 39 (1), 105–110.Google Scholar
Lin, Y.-C., Gfoehler, M. and Pandy, M.G. (2014). Quantitative evaluation of the major determinants of human gait. Journal of Biomechanics, 47 (6), 1324–1331.Google Scholar
McHugh, M.L. (2013). The Chi-square test of independence. Biochemia Medica, 23 (2), 143–149.Google Scholar
Michael, R.S. (2001). Crosstabulation & Chi square. Indiana University. http://www.indiana.edu/~educy520/sec5982/week_12/chi_sq_summary011020.pdf. Accessed 11 August 2016.Google Scholar
Muhammad, M.N., Salcic, Z. and Wang, K.I.K. (2015). Real-time PDR based on resource-constrained embedded platform. 2015 IEEE 9th International Conference on Sensing Technology, Auckland, 779–784.Google Scholar
Muhammad, M.N., Salcic, Z. and Wang, K.I.-K. (2014). Subtractive Clustering as Zupt Detector. 2014 IEEE 11th International Conference on Ubiquitous Intelligence and Computing, Bali, 349–355.Google Scholar
Pavčič, J., Matjaèiæ, Z. and Olenšek, A. (2014). Kinematics of turning during walking over ground and on a rotating treadmill. Journal of NeuroEngineering and Rehabilitation, 11 (1), 127.Google Scholar
Saunders, J.B. dec. M., Inman, V.T. and Eberhart, H.D. (1953). The major determinants in normal and pathological gait. The Journal of Bone & Joint Surgery, 35 (3), 543–558.Google Scholar
Schumacker, R. and Tomek, S. (2013). Understanding Statistics Using R. Springer-Verlag New York, 169–175.CrossRefGoogle Scholar
Tian, Q., Salcic, Z., Wang, K.I.-K. and Pan, Y. (2015). A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones. Sensors, 15 (12), 30759–30783.Google Scholar
Vincent, D. (2013). Accurate Position Tracking Using Inertial Measurement Units [White paper]. https://www.pnicorp.com/wp-content/uploads/Accurate-PositionTracking-Using-IMUs.pdf. Accessed 8 August 2016.Google Scholar
Zhang, S., Huang, Q., Wang, H., Xu, W., Ma, G., Liu, Y. and Yu, Z. (2013). The Mechanism of Yaw Torque Compensation in the Human and Motion Design for Humanoid Robots. International Journal of Advanced Robotic Systems, 10(1).CrossRefGoogle Scholar