Position Tracking During Human Walking Using an Integrated Wearable Sensing System

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.

[1]  Abdelmoumen Norrdine,et al.  Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet , 2016, IEEE Sensors Journal.

[2]  Hai Yang,et al.  Adaptive Zero Velocity Update Based on Velocity Classification for Pedestrian Tracking , 2017, IEEE Sensors Journal.

[3]  Susanna Kaiser,et al.  Performance comparison of foot- and pocket-mounted inertial navigation systems , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[4]  Maximilian Schirmer,et al.  Shoe me the Way: A Shoe-Based Tactile Interface for Eyes-Free Urban Navigation , 2015, MobileHCI.

[5]  Naser El-Sheimy,et al.  A new multi-position calibration method for MEMS inertial navigation systems , 2007 .

[6]  Miguel A. Labrador,et al.  Multi sensor system for pedestrian tracking and activity recognition in indoor environments , 2016, Int. J. Ad Hoc Ubiquitous Comput..

[7]  C. Jekeli Inertial navigation systems with geodetic applications , 2000 .

[8]  Henry Martin Overcoming the challenges of low-cost inertial navigation , 2016 .

[9]  Yeng Chai Soh,et al.  Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections , 2016, IEEE Transactions on Industrial Informatics.

[10]  Isaac Skog,et al.  Fusing the information from two navigation systems using an upper bound on their maximum spatial separation , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[11]  Mark Newman,et al.  The Limits of In-run Calibration of MEMS and the Effect of New Techniques , 2014 .

[12]  Laurent Itti,et al.  Walking compass with head-mounted IMU sensor , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[14]  Eric Foxlin,et al.  Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.

[15]  苏中,et al.  Wearable Indoor Pedestrian Navigation Based on MIMU and Hypothesis Testing , 2015 .

[16]  Robert Harle,et al.  Pedestrian localisation for indoor environments , 2008, UbiComp.

[17]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[18]  Isaac Skog,et al.  Calibration of a MEMS inertial measurement unit , 2006 .

[19]  Jonathan Kelly,et al.  Improving foot-mounted inertial navigation through real-time motion classification , 2017, 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[20]  Yun Pan,et al.  A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones , 2016, IEEE Sensors Journal.

[21]  Muhammad Haris Afzal,et al.  New method for magnetometers based orientation estimation , 2010, IEEE/ION Position, Location and Navigation Symposium.

[22]  Young Soo Suh,et al.  Pedestrian Navigation Using Foot-Mounted Inertial Sensor and LIDAR , 2016, Sensors.

[23]  Nan Li,et al.  A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors , 2016, Sensors.

[24]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[25]  Terry Moore,et al.  Particle filter for context sensitive indoor pedestrian navigation , 2016, 2016 International Conference on Localization and GNSS (ICL-GNSS).

[26]  William S. Murphy,et al.  Determination of a Position in Three Dimensions using Trilateration and Approximate Dis- tances , 1995 .

[27]  Willy Hereman,et al.  Statistical methods in surveying by trilateration , 1998 .

[28]  Andrew Y. C. Nee,et al.  Methods for in-field user calibration of an inertial measurement unit without external equipment , 2008 .

[29]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[30]  Hongyu Guo,et al.  A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System , 2016, Sensors.

[31]  Martin Klepal,et al.  A Backtracking Particle Filter for fusing building plans with PDR displacement estimates , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.