Real-life Measurement of Tri-axial Walking Ground Reaction Forces using Optimal Network of Wearable Inertial Measurement Units
Shahabpoor, E; Brownjohn, JMW; Billings, SA; et al.Guo, L-Z; Bocian, M
Date: 3 May 2018
Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Monitoring natural human gait in real-life
environment is essential in many applications including
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 ...
Monitoring natural human gait in real-life
environment is essential in many applications including
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 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.
Engineering
Faculty of Environment, Science and Economy
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