Learning Individual Models to Estimate the Walking Direction of Mobile Phone Users

Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device's pointing direction...

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Bibliographic Details
Published in:IEEE sensors journal 2019-12, Vol.19 (24), p.12306-12315
Main Authors: Perul, Johan, Renaudin, Valerie
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Cell phones
Cellular telephones
Dead reckoning
Engineering Sciences
Errors
Estimation
Gait
gait analysis
Gaussian mixture model (GMM)
inertial sensors
Legged locomotion
Magnetometers
Modelling
Navigation
Pedestrian navigation
Performance evaluation
Probabilistic models
Sensors
Straight lines
Walking
walking direction
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Summary:Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device's pointing direction. A better understanding of human walking gait has enabled to produce new algorithms to mitigate the impact of the way the device is held in hand. WAISS algorithm is one of them. It is based on the study of horizontal hand accelerations and their modeling using Gaussian Mixture Models (GMM). However, ongoing search for universal modeling of handheld device carrying mode defeats the varying nature of human gait. This paper investigates the impact of individual gait characteristics and their modeling to improve the estimation of the walking direction. Different models are learned for curved and straight lines and varying GMM are proposed to account for inter-individual gait variations. This results in a reduced walking direction error with a 8.1° mean error to the 90th percentile computed for 3 subjects over a 1.5 km indoor/outdoor walk.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2940138