Benchmarking foot trajectory estimation methods for mobile gait analysis

Julius Hannink*, Malte Ollenschläger, Felix Kluge, Nils Roth, Jochen Klucken, Bjoern M. Eskofier

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Article number1940
JournalSensors
Volume17
Issue number9
DOIs
Publication statusPublished - Sep 2017
Externally publishedYes

Keywords

  • Benchmark dataset
  • Clinical gait analysis
  • Double integration
  • Human gait
  • Orientation estimation
  • Wearable sensors

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