The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks

Gurchiek, Reed D.; McGinnis, Ryan S.; Needle, Alan R.; McBride, Jeffrey M.; Werkhoven, Herman van

doi:10.1016/j.jbiomech.2017.07.035

Cited by 57 publications

(60 citation statements)

References 16 publications

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“…Gurchiek et al [ 56 ] conducted a feasibility study regarding the use of a single IMU placed on the sacrum. The IMU was placed close to the centre of mass in order to measure translational acceleration at this point.…”

Section: Discussionmentioning

confidence: 99%

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Ancillao

Tedesco

Barton

et al. 2018

Sensors

158

146

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In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

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Section: Discussionmentioning

confidence: 99%

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Ancillao

Tedesco

Barton

et al. 2018

Sensors

158

146

View full text Add to dashboard Cite

show abstract

“…between acceleration signal of CoM of each segment 'i' and the corresponding IMU signals measured at C7, L5 and one of the thighs (denoted as ) were calculated using (4). Model 1 proposes to use (10)- (12), in each direction 'j', to calculate the coefficients in (8):…”

Section: A Model 1 For Each Direction 'J' the Pearson Cross-correlamentioning

confidence: 99%

“…where, is the gravitational acceleration. Later in 2017, Gurchiek, et al, [4] showed that a single IMU at Sacrum can be used to estimate the tri-axial signals during accelerative running. These pioneering works highlight the possibility of estimating signals from limited number of IMU measurements.…”

Section: Introductionmentioning

confidence: 99%

Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units

Shahabpoor

Pavić

Billings

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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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.

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“…These studies differ in terms of sensor configuration, sensor location, and type of parameter measured [1]. Several groups have used inertial sensors in sprint running to characterize temporal parameters [2][3][4], body-segment orientation [5,6], ground reaction forces [7,8], and speed [9][10][11]. Surprisingly, only a few studies used MIMU to quantify spatiotemporal parameters in hurdle races.…”

Section: Introductionmentioning

confidence: 99%

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

Falbriard

Mohr

Aminian

2020

Sensors

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This research aimed to determine whether: (1) shoe-worn magnetic and inertial sensors can be used to detect hurdle clearance and identify the leading leg in 400-m hurdles, and (2) to provide an analysis of the hurdlers’ spatiotemporal parameters in the intervals defined by the hurdles’ position. The data set is composed of MIMU recordings of 15 athletes in a competitive environment. The results show that the method based on the duration of the flight phase was able to detect hurdle clearance and identify the leading leg with 100% accuracy. Moreover, by combining the swing phase duration with the orientation of the foot, we achieved, in unipedal configuration, 100% accuracy in hurdle clearance detection, and 99.7% accuracy in the identification of the leading leg. Finally, this study provides statistical evidence that contact time significantly increases, while speed and step frequency significantly decrease with time during 400 m hurdle races.

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The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks

Cited by 57 publications

References 16 publications

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

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