Dataset of lower extremity joint angles, moments and forces in distance running

Mei, Qichang; Fernandez, Justin; Xiang, Liangliang; Gao, Zixiang; Yu, Peimin; Baker, Julien S.; Gu, Yaodong

doi:10.1016/j.heliyon.2022.e11517

Cited by 6 publications

(6 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a secondary data analysis of a musculoskeletal modelling study of walking and running, the data of which, is publicly available ( Mei et al, 2022 ). Herein, we summarise the experimental procedure used to collect the data, with specific details provided in the main article ( Mei et al, 2022 ). Figure 1 represents a workflow of the methods employed in this study.…”

Section: Methodsmentioning

confidence: 99%

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks

Liew

Rügamer

Mei

et al. 2023

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating in-vivo forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.

show abstract

Section: Methodsmentioning

confidence: 99%

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks

Liew

Rügamer

Mei

et al. 2023

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…trc) ( Zhou et al, 2021 ). The model Mei et al (2022) used in this experiment was adapted from the original OpenSim model ( Rajagopal et al, 2016 ) by Mei et al (2022) . The model was upgraded with additional ranges of motion in the coronal and horizontal planes of the knee joint.…”

Section: Methodsmentioning

confidence: 99%

Biomechanical effects of exercise fatigue on the lower limbs of men during the forward lunge

Gao,

Ye,

Bálint

et al. 2023

Front. Physiol.

Self Cite

View full text Add to dashboard Cite

Background: During competition and training, exercises involving the lungs may occur throughout the sport, and fatigue is a major injury risk factor in sport, before and after fatigue studies of changes in the lungs are relatively sparse. This study is to investigate into how fatigue affects the lower limb’s biomechanics during a forward lunge.Methods: 15 healthy young men participate in this study before and after to exposed to a fatigue protocol then we tested the forward lunge to obtain kinematic, kinetic changing during the task, and to estimate the corresponding muscles’ strength changes in the hip, knee, and ankle joints. The measurement data before and after the fatigue protocol were compared with paired samples t-test.Results: In the sagittal and horizontal planes of the hip and knee joints, in both, the peak angles and joint range of motion (ROM) increased, whereas the moments in the sagittal plane of the knee joint smaller. The ankle joint’s maximum angle smaller after fatigue. Peak vertical ground reaction force (vGRF) and peak contact both significantly smaller after completing the fatigue protocol and the quadriceps mean and maximum muscular strength significantly increased.Conclusion: After completing a fatigue protocol during lunge the hip, knee, and ankle joints become less stable in both sagittal and horizontal planes, hip and knee range of motion becomes greater. The quadriceps muscles are more susceptible to fatigue and reduced muscle force. Trainers should focus more on the thigh muscle groups.

show abstract

“…Cameras and laboratory setups were calibrated and noisereduced before each experiment. Reflective markers on the subject were simultaneously captured by at least two cameras throughout the movement [23]. Simultaneous acquisition of EMG signals from TA, PL, GM and GL at 1000 Hz using a wireless Delsys EMG test system (Delsys, Boston, MA, USA) for data processing and OpenSim model validation [24].…”

Section: Instrumentsmentioning

confidence: 99%

The Effect of Short-Term Neuromuscular Training on Ankle Biomechanics in Individuals with Functional Ankle Instability

Zhao,

Shao,

Teo

et al. 2024

Preprint

View full text Add to dashboard Cite

Functional ankle instability (FAI) results from multiple ankle sprains, causing a perceived sense of joint instability due to muscle and ligament imbalances. Neuromuscular training (NMT) is employed to enhance ankle stability in individuals with FAI. The study included 24 male university students with FAI, who underwent ankle NMT on three surfaces. OpenSim musculoskeletal model analyzed the effects on ankle muscle activity, kinetics, and kinematics. Utilizing repeated measures analysis of variance (ANOVA) and one-dimensional statistical non-parametric mapping (SNPM1d) to identify distinctions between training surfaces. NMT on foam cushion (FC) significantly enhanced tibialis anterior (TA) and gastrocnemius medial (GM) activation during walking compared to level-ground (LG) and artificial turf (AT) (p&lt;0.05). However, no differences observed for peroneus longus (PL), gastrocnemius lateral (GL), and peak ankle plantarflexion (PF). During jogging and fast running, post FC training increased TA, PL, GM, GL activation, and peak ankle PF moment (p&lt;0.05). Additionally, post FC training, subtalar mobility was significantly reduced (p&lt;0.05), and ankle joints exhibited greater dorsiflexion (DF) angles during jogging and fast running (p&lt;0.05). Consequently, selecting FC for short-term ankle NMT proves more advantageous in enhancing ankle stability among individuals with FAI, yielding a more positive impact on FAI improvement.

show abstract

Dataset of lower extremity joint angles, moments and forces in distance running

Cited by 6 publications

References 91 publications

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks

Biomechanical effects of exercise fatigue on the lower limbs of men during the forward lunge

The Effect of Short-Term Neuromuscular Training on Ankle Biomechanics in Individuals with Functional Ankle Instability

Contact Info

Product

Resources

About