Predicting Ground Reaction Forces in Sprint Running Using a Shank Mounted Inertial Measurement Unit

Thiel, David V.; Shepherd, Jonathan B.; Espinosa, Hugo G.; Kenny, Megan; Fischer, Katrien; Worsey, Matthew T. O.; Matsuo, Akifumi; Wada, Tomohito

doi:10.3390/proceedings2060199

Cited by 20 publications

(27 citation statements)

References 6 publications

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“…Thiel et al [ 62 ] tested the GRF during sprint running by means of IMUs. They used two IMUs composed of 3D accelerometer, gyroscope and magnetometer.…”

Section: Discussionmentioning

confidence: 99%

“…The coefficients were determined for each foot by taking advantage of the force recorded in the first steps [ 62 ]. This kind of modelling was proved suitable for the first stages of the sprint where a constant GRF is expected and therefore a linear modelling is suitable [ 62 ]. In addition, the shank angular velocity was used to identify stance and swing phases and, as a consequence, to identify timings of the peak GRF.…”

Section: Discussionmentioning

confidence: 99%

“… The vertical GRF measured by the force plate (red) compared to the one predicted by the IMUs (blue) for one subject, according to the method by [ 62 ]. …”

Section: Figurementioning

confidence: 99%

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

Ancillao

Tedesco

Barton

et al. 2018

Sensors

179

167

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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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“…Thiel et al [ 62 ] tested the GRF during sprint running by means of IMUs. They used two IMUs composed of 3D accelerometer, gyroscope and magnetometer.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ancillao

Tedesco

Barton

et al. 2018

Sensors

179

167

View full text Add to dashboard Cite

show abstract

“…The change of kinematics due to fatigue was similar to that under load conditions. IMUs on the lower body were used to estimate joint kinematics and GRF during running [47,48]. Combining single IMU data from the sacrum with a convolutional neural network, Johnson et al estimated 3D GRF with the error of 4-9% during running and side-stepping [14].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Lim

Kim

Park

2019

Sensors

106

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Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

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“…The inertial sensor technology used was manufactured in-house and contains a three-axis accelerometer (±16 g), three-axis gyroscope (±2000 • /s), and magnetometer (±7 Gauss) (SABELSense, Griffith University, Nathan, Australia [20,[28][29][30]). The sampling rate of the sensor was set to 250 Hz-the highest capture speed for these units.…”

Section: Inertial Sensorsmentioning

confidence: 99%

Assessment of Head Impacts and Muscle Activity in Soccer Using a T3 Inertial Sensor and a Portable Electromyography (EMG) System: A Preliminary Study

et al. 2020

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Heading the ball is an important skill in soccer. Head impacts are of concern because of the potential adverse health effects. Many elite players now wear GPS (that include inertial monitoring units) on the upper spine for location tracking and workload measurement. By measuring the maximum acceleration of the head and the upper spine, we calculated the acceleration ratio as an attenuation index for participants (n = 8) of different skill levels during a front heading activity. This would allow for in-field estimates of head impacts to be made and concussive events detected. For novice participants, the ratio was as high as 8.3 (mean value 5.0 ± 1.8), whereas, for experienced players, the mean ratio was 3.2 ± 1.5. Elite players stiffen the neck muscles to increase the ball velocity and so the torso acts as a support structure. Electromyography (EMG) signals that were recorded from the neck and shoulder before and after a training intervention showed a major increase in mean average muscle activity (146%, p = 3.39 × 10−6). This was accompanied by a major decrease in acceleration ratio (34.41%, p = 0.008). The average head-ball impact velocity was 1.95 ± 0.53 m/s determined while using optical motion capture. For this low velocity, the impact force was 102 ± 19 N, 13% of the published concussive force. The voluntary action of neck muscles decreases isolated head movements during heading. Coaches and trainers may use this evidence in their development of junior players.

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Predicting Ground Reaction Forces in Sprint Running Using a Shank Mounted Inertial Measurement Unit

Cited by 20 publications

References 6 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

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Assessment of Head Impacts and Muscle Activity in Soccer Using a T3 Inertial Sensor and a Portable Electromyography (EMG) System: A Preliminary Study

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