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. 2017 Dec 10;17(12):2866.
doi: 10.3390/s17122866.

Position Tracking During Human Walking Using an Integrated Wearable Sensing System

Giulio Zizzo  1 , Lei Ren  2
Affiliations

Position Tracking During Human Walking Using an Integrated Wearable Sensing System

Giulio Zizzo et al. Sensors (Basel). .

Abstract

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.

Keywords: IMU navigation; Kalman filter; pedestrian dead reckoning; wearable sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
(a) The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 2 of Type 1 walking compared to the ground truth position measured simultaneously by a Vicon motion capture system. (b) The absolute errors of the IMU, IMU/US and IUP systems at each single step for trial 2 of Type 1 walking. (c) The percentage errors of the IMU, IMU/US and IUP systems at each single step for trial 2 of Type 1 walking.
Figure A2
Figure A2
(a) The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 3 of Type 1 walking compared to the ground truth position measured simultaneously by a Vicon motion capture system. (b) The absolute errors of the IMU, IMU/US and IUP systems at each single step for trial 3 of Type 1 walking. (c) The percentage errors of the IMU, IMU/US and IUP systems at each step for trial 3 of Type 1 walking. In this trial, the percentage error was initially high. This was probably due to the error accumulation in the first step. However, we can see a drop off in the percentage error as the periodic walking pattern began.
Figure A3
Figure A3
The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 2 of Type 2 walking.
Figure A4
Figure A4
The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 3 of Type 2 walking.
Figure 1
Figure 1
The setup of the ultrasound sensors. The ultrasound sensors marked in red are active when the right leg is the leading leg during walking (a). The ultrasound sensors marked in blue are active when the left leg is the leading leg during walking (b).
Figure 2
Figure 2
Block diagram of the inertial measurement unit and ultrasound sensors (IMU/US) system setup. The infrared (IR) LEDs are used to synchronise the ultrasound transmitters and ultrasound receivers. The sensors are connected to an ATMega328P micro-controller, which relays the information to a desktop PC for data-processing.
Figure 3
Figure 3
Both (a) and (b) demonstrate how the particle cloud can diverge. This clearly shows how computing a direct average will yield inaccurate results, as in this case it will give a location situated in an impassible terrain feature. By applying a clustering algorithm, it is possible to exclude the smaller particle cloud from influencing the calculated position.
Figure 4
Figure 4
(a) The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 1 of Type 1 walking compared to the ground truth position measured simultaneously by a Vicon motion capture system. (b) The absolute errors of the IMU, IMU/US and IUP systems at each single step for trial 1 of Type 1 walking. (c) The percentage errors of the IMU, IMU/US and IUP systems at each single step for trial 1 of Type 1 walking.
Figure 5
Figure 5
The wearable sensing system during Type 1 walking. The left foot is on the ground when the zero-velocity updates (ZUPT) and Heuristic Drift Reduction (HDR) corrections are applied.
Figure 6
Figure 6
The tracked walking paths by the inertial measurement unit (IMU), IMU/ultrasound (US) and inertial/ultrasound/particle filter (IUP) systems for trial 1 of Type 2 walking.
See this image and copyright information in PMC

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