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Probabilistic Context-aware Step Length Estimation for Pedestrian Dead Reckoning

Martinelli, A; Gao, H; Groves, PD; Morosi, S; (2018) Probabilistic Context-aware Step Length Estimation for Pedestrian Dead Reckoning. IEEE Sensors Journal , 18 (4) pp. 1600-1611. 10.1109/JSEN.2017.2776100. Green open access

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Abstract

This paper introduces a weighted context-based step length estimation algorithm for pedestrian dead reckoning. Six pedestrian contexts are considered: stationary, walking, walking sideways, climbing and descending stairs, and running. Instead of computing the step length based on a single context, the step lengths computed for different contexts are weighted by the context probabilities. This provides more robust performance when the context is uncertain. The proposed step length estimation algorithm is part of a pedestrian dead reckoning system which includes the procedures of step detection and context classification. The step detection algorithm detects the step time boundaries using continuous wavelet transform analysis, while the context classification algorithm determines the pedestrian context probabilities using a relevance vector machine. In order to assess the performance of the pedestrian dead reckoning system, a data set of pedestrian activities and actions has been collected. Fifteen subjects have been equipped with a waist-belt smartphone and traveled along a predefined path. Acceleration, angular rate and magnetic field data were recorded. The results show that the traveled distance is more accurate using step lengths weighted by the context probabilities compared to using step lengths based on the highest probability context.

Type: Article
Title: Probabilistic Context-aware Step Length Estimation for Pedestrian Dead Reckoning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSEN.2017.2776100
Publisher version: https://doi.org/10.1109/JSEN.2017.2776100
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Legged locomotion, Estimation, Dead reckoning, Force, Magnetic sensors, Step length estimation, context detection, step detection, pedestrian dead reckoning navigation
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10038133
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