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Sensors 2017, 17(9), 1940; doi:10.3390/s17091940

Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis

1
Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
2
Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
*
Author to whom correspondence should be addressed.
Received: 19 July 2017 / Revised: 18 August 2017 / Accepted: 19 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Abstract

Mobile gait analysis systems based on inertial sensing on the shoe are applied in a wide range of applications. Especially for medical applications, they can give new insights into motor impairment in, e.g., neurodegenerative disease and help objectify patient assessment. One key component in these systems is the reconstruction of the foot trajectories from inertial data. In literature, various methods for this task have been proposed. However, performance is evaluated on a variety of datasets due to the lack of large, generally accepted benchmark datasets. This hinders a fair comparison of methods. In this work, we implement three orientation estimation and three double integration schemes for use in a foot trajectory estimation pipeline. All methods are drawn from literature and evaluated against a marker-based motion capture reference. We provide a fair comparison on the same dataset consisting of 735 strides from 16 healthy subjects. As a result, the implemented methods are ranked and we identify the most suitable processing pipeline for foot trajectory estimation in the context of mobile gait analysis. View Full-Text
Keywords: wearable sensors; human gait; clinical gait analysis; benchmark dataset; orientation estimation; double integration wearable sensors; human gait; clinical gait analysis; benchmark dataset; orientation estimation; double integration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hannink, J.; Ollenschläger, M.; Kluge, F.; Roth, N.; Klucken, J.; Eskofier, B.M. Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis. Sensors 2017, 17, 1940.

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