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Open AccessArticle

Position Tracking During Human Walking Using an Integrated Wearable Sensing System

by Giulio Zizzo and Lei Ren *
School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
*
Author to whom correspondence should be addressed.
Current address: Imperial College London, London SW7 2AZ, UK.
Sensors 2017, 17(12), 2866; https://doi.org/10.3390/s17122866
Received: 10 November 2017 / Revised: 3 December 2017 / Accepted: 8 December 2017 / Published: 10 December 2017
Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4–5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps. View Full-Text
Keywords: Kalman filter; pedestrian dead reckoning; wearable sensors; IMU navigation Kalman filter; pedestrian dead reckoning; wearable sensors; IMU navigation
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Zizzo, G.; Ren, L. Position Tracking During Human Walking Using an Integrated Wearable Sensing System. Sensors 2017, 17, 2866.

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