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Communication Dans Un Congrès Année : 2023

A Computer Vision Approach for Pedestrian Walking Direction Estimation with Wearable Inertial Sensors: PatternNet

Résumé

In this paper, we propose an image-based neural network approach (PatternNet) for walking direction estimation with wearable inertial sensors. Gait event segmentation and projection are used to convert the inertial signals to image-like tabular samples, from which a Convolutional Neural Network (CNN) extracts geometrical features for walking direction inference. To embrace the diversity of individual walking characteristics and different ways to carry the device, tailor-made models are constructed based on individual users' gait characteristics and the device-carrying mode. Experimental assessments of the proposed method and a competing method (RoNIN) are carried out in reallife situations and over 3 km total walking distance, covering indoor and outdoor environments, involving both sighted and visually impaired volunteers carrying the device in three different ways: texting, swinging and in a jacket pocket. PatternNet estimates the walking directions with a mean accuracy between 7 to 10 degrees for the three test persons and is 1.5 times better than RONIN estimates.
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Dates et versions

hal-04152049 , version 1 (05-07-2023)

Identifiants

Citer

Hanyuan Fu, Thomas Bonis, Valerie Renaudin, Ni Zhu. A Computer Vision Approach for Pedestrian Walking Direction Estimation with Wearable Inertial Sensors: PatternNet. 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS 2023), Institute of Electrical and Electronics Engineers (IEEE), Apr 2023, Monterey, CA, United States. pp.691-699, ⟨10.1109/plans53410.2023.10140028⟩. ⟨hal-04152049⟩
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