Real-life measurement of tri-axial walking ground reaction forces using optimal network of wearable inertial measurement units

Erfan Shahabpoor Ardakani, Aleksandar Pavic, James M.W. Brownjohn, Stephen A. Billings, Ling Zhong Guo, Mateusz Bocian

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
59 Downloads (Pure)

Abstract

Monitoring natural human gait in real-life environment is essential in many applications including the quantification of disease progression, and monitoring the effects of treatment and alteration of performance biomarkers in professional sports. Nevertheless, reliable and practical techniques and technologies necessary for continuous real-life monitoring of gait is still not available. This paper explores in detail the correlations between the acceleration of different body segments and walking ground reaction forces GRF(t) in three dimensions and proposes three sensory systems, with one, two, and three inertial measurement units (IMUs), to estimate GRF(t) in the vertical (V), medial-lateral (ML), and anterior-posterior (AP) directions. The nonlinear autoregressive moving average model with exogenous inputs (NARMAX) non-linear system identification method was utilized to identify the optimal location for IMUs on the body for each system. A simple linear model was then proposed to estimate GRF(t) based on the correlation of segmental accelerations with each other. It was found that, for the three-IMU system, the proposed model estimated GRF(t) with average peak-to-peak normalized root mean square error (NRMSE) of 7%, 16%, and 18% in V, AP, and ML directions, respectively. With a simple subject-specific training at the beginning, these errors were reduced to 7%, 13%, and 13% in V, AP, and ML directions, respectively. These results were found favorably comparable with the results of the benchmark NARMAX model, with subject-specific training, with 0% (V), 4% (AP), and 1% (ML) NRMSE difference.

Original languageEnglish
Pages (from-to)1243-1253
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume26
Issue number6
Early online date3 May 2018
DOIs
Publication statusPublished - 6 Jun 2018

Keywords

  • Ambulation
  • Biomechanics
  • Black-box approach
  • Gait monitoring
  • Outdoor measurement

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

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