Morphology-Related Foot Function Analysis: Implications for Jumping and Running

Yu, Peimin; Xiang, Liangliang; Liang, Minjun; Mei, Qichang; Baker, Julien S.; Gu, Yaodong

doi:10.3390/app9163236

Cited by 4 publications

(2 citation statements)

References 51 publications

(89 reference statements)

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“…The ML models’ performance were compared based on their resulting RMSE, MAE, and R 2 against the OpenSim and CEINMS output (used here as ground truth). The computed OpenSim joint angles and moments waveforms found in this study were similar to the literature 49 , 50 . Muscle forces computation were validated by comparing them to the experimental EMG recordings.…”

Section: Discussionsupporting

confidence: 85%

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

Moghadam

Yeung

Choisne

2023

Sci Rep

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A combination of wearable sensors’ data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints’ kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys’ (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors’ data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets’ prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors’ data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.

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Section: Discussionsupporting

confidence: 85%

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

Moghadam

Yeung

Choisne

2023

Sci Rep

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“…Ridge et al 10 found that larger peak flexion angles in knee and hip joint during UGT at FWS than NWS. Larger sagittal knee ROM may be a compensatory movement due to the increased gait speed 20 , resulting from greater knee impact during UGT. Subjects stabilized with larger range of hip, knee, and ankle joints motion, which may contribute to faster terminating times, but may also need greater joint extensor activity for stability 21 .…”

Section: Discussionmentioning

confidence: 99%

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Zhou

Cen

Song

et al. 2020

JoVE

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Editorial “Biomechanical Spectrum of Human Sport Performance”

Taı̈ar

Bernardo-Filho

2020

Applied Sciences

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Several parameters can influence our health capital today and can have a negative impact on our performance, whether physiological or mechanical. Indeed, our health and wellbeing are influenced by a range of social, cultural, economic, psychological, and environmental factors across our lives. These change as we progress through the key transition points in life—from infancy and childhood through our teenage years to adulthood, working life, retirement and the end of life. Sport can be a vector that links many of these factors. Whether it is high-performance sport or sedentary practice, sport is very important for the improvement of psychological wellbeing and physical health. Our overarching aim was to increase quality of life. Sedentary practice can increase mobility and reduce the risk of disease, so changing adults’ behavior through sedentary practice could reduce illness and decrease costs to society concerning health problems. Furthermore, a higher frequency of practice can lead to improvements in technique and optimized performance. Our objective is to summarize the latest research in sport science and to quantify the most important parameters influencing human performance related to the health sciences for all age groups, throughout their lives.

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Morphology-Related Foot Function Analysis: Implications for Jumping and Running

Cited by 4 publications

References 51 publications

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Editorial “Biomechanical Spectrum of Human Sport Performance”

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