# Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

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## Abstract

**:**

## 1. Introduction

## 2. Proposed Position Estimation Method

#### 2.1. Stance Phase Detection

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#### 2.2. Motion-Induced Linear Acceleration Derivation

#### 2.3. ZUPT and Process Model

#### 2.4. Measurement Model

#### 2.5. Particle Filtering

## 3. Experimental Results

#### 3.1. Experimental Setup

#### 3.2. Experimental Results and Discussion

## 4. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**The attachment of a sensor module on the foot of a subject and the illustration of the coordinate systems. The body coordinate system is given in red dashed lines, while the sensor coordinate system is given in black solid lines.

**Figure 3.**Illustration of the lower body movement during a gait cycle. (

**a**) Take the leg in green for example, a gait cycle mainly consists of 6 steps, i.e., (1) the heel leaves the ground (heel off), (2) the toe leaves ground (toe-off), (3) the leg swings forward, (4) the heel contacts the ground (heel-strike), (5) the toe contacts the ground (toe-strike), (6) the stance phase when foot stays on the ground to support the other leg swing forward; (

**b**) The gyroscope signal in the sagittal plane during a gait cycle, where the gyroscope signal is close to 0 and the variations are subtle during the stance phase.

**Figure 4.**One example of the position estimation results for indoor experiments. Total walking distance was 132 m, at a speed about 0.75 m/s. The actual positions of the 9 critical points were marked by the square number plates. It is obvious that the integration of ZUPT and map information can improve the position estimation accuracy significantly.

**Figure 5.**Indoor walking: the average and standard deviation of distance errors between the estimated positions and actual positions of the 9 critical points over 5 trials. For the traditional methods without map information, the distance errors had significant increment as the walking distance increased due to acceleration integration drift.

**Figure 6.**One example of the position estimation for outdoor experiments with a walking speed at about 0.75 m/s. The actual positions of the 8 critical points were marked by the square number plates. It is obvious that the integration of ZUPT and map information can further improve the position estimation accuracy.

**Figure 7.**Outdoor walking: the average and standard deviation of distance errors between the estimated positions and actual positions of the 8 critical points over 5 trials. For the traditional methods without map information, the distance errors had significant increment as the walking distance increased due to acceleration integration drift.

**Figure 8.**Comparative results with our previous methods presented in [10] and [12]. (

**a**) Indoor scenario; and (

**b**) Outdoor scenario. For both figures, “ZUPT+SC” is for fusion of ZUPT and step counter presented in [10], “SC” is for the step counter method presented in [12], and “ZUPT+Map” is for fusion of ZUPT and map proposed in this paper.

**Figure 9.**Examples of the estimated position results for walking in a circle with radius of 3 m, where no map information was provided.

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## Share and Cite

**MDPI and ACS Style**

Bao, S.-D.; Meng, X.-L.; Xiao, W.; Zhang, Z.-Q.
Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking. *Sensors* **2017**, *17*, 340.
https://doi.org/10.3390/s17020340

**AMA Style**

Bao S-D, Meng X-L, Xiao W, Zhang Z-Q.
Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking. *Sensors*. 2017; 17(2):340.
https://doi.org/10.3390/s17020340

**Chicago/Turabian Style**

Bao, Shu-Di, Xiao-Li Meng, Wendong Xiao, and Zhi-Qiang Zhang.
2017. "Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking" *Sensors* 17, no. 2: 340.
https://doi.org/10.3390/s17020340