Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Shao, Wenhua; Luo, Haiyong; Zhao, Fang; Crivello, Antonino

doi:10.1177/1550147718803072

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…Therefore, in addition to providing more accurate motion level estimation, precise stride length estimation based on built-in smartphone inertial sensors enhances positioning accuracy of PDR. Most visible light positioning [5,6], Wi-Fi positioning [7][8][9], and magnetic positioning [10][11][12] critically depend on PDR. Hence, motion level estimation based on smartphones contributes to assisting and supporting patients undergoing health rehabilitation and treatment, activity monitoring of daily living, navigation, and numerous other applications [13].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

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Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

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