Inter-limb differences in in-vivo tendon behavior, kinematics, kinetics and muscle activation during running

Jacques, Tiago Canal; Bini, Rodrigo Rico; Arndt, Anton

doi:10.1016/j.jbiomech.2022.111209

Cited by 3 publications

(3 citation statements)

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“…The measured average normalized EMG activities of the soleus and gastrocnemius medialis muscles during running were used for the muscle activation. Several studies [30,31] reported similar activation patterns of gastrocnemius medialis and gastrocnemius lateralis at running speeds similar to the ones in the present study. Therefore, in the sensitivity analysis, similar activation of the two muscles was assumed.…”

Section: Sensitivity Analysissupporting

confidence: 89%

Biarticular mechanisms of the gastrocnemii muscles enhance ankle mechanical power and work during running

Arampatzis,

Kharazi,

Theodorakis

et al. 2023

R. Soc. open sci.

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The objective of the study was to explore how biarticular mechanisms of the gastrocnemii muscles may provide an important energy source for power and work at the ankle joint with increasing running speed. Achilles tendon force was quantified as a proxy of the triceps surae muscle force and the contribution of the monoarticular soleus and the biarticular gastrocnemii to the mechanical power and work performed at the ankle joint was investigated in three running speeds (transition 2.0 m s −1 , slow 2.5 m s −1 , fast 3.5 m s −1 ). Although the contribution of the soleus was higher, biarticular mechanisms of the gastrocnemii accounted for a relevant part of the performed mechanical power and work at the ankle joint. There was an ankle-to-knee joint energy transfer in the first part of the stance phase and a knee-to-ankle joint energy transfer during push-off via the gastrocnemii muscles, which made up 16% of the total positive ankle joint work. The rate of knee-to-ankle joint energy transfer increased with speed, indicating a speed-related participation of biarticular mechanisms in running. This energy transfer via the gastrocnemii seems to occur with negligible energy absorption/production from the quadriceps vasti contractile elements and is rather an energy exchange between elastic structures.

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Section: Sensitivity Analysissupporting

confidence: 89%

Biarticular mechanisms of the gastrocnemii muscles enhance ankle mechanical power and work during running

Arampatzis,

Kharazi,

Theodorakis

et al. 2023

R. Soc. open sci.

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“…Similar features could be shown for team behavior in volleyball (Westphal and Schöllhorn, 2001;Jäger and Schöllhorn, 2007), soccer (Grunz et al, 2012;Rein and Memmert, 2016), or basketball (Schmidt, 2012;Kempe et al, 2014). Typical parameters investigated are biomechanical data from video recordings (Kaur et al, 2023), force plates (Horst et al, 2016;Horst et al, 2017a;Horst et al, 2017b), pressure insoles (Schöllhorn et al, 2002b;Jacques et al, 2022), EMG (Jaitner et al, 2001;Aeles et al, 2021), and brain signals (Liao et al, 2014;Lee et al, 2022). Besides these, wearable sensors are becoming increasingly popular (Hammad and El-Sankary, 2020).…”

Section: Introductionmentioning

confidence: 71%

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Burdack

Giesselbach

Simak

et al. 2023

Front. Bioeng. Biotechnol.

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In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.

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“…Similar features could be shown for team behavior in volleyball [32], [33], soccer [34], [35], or basketball [36], [37]. Typical parameters investigated are biomechanical data from video recordings [38], force plates [39]- [41], pressure insoles [42], [43], EMG [44], [45], and brain signals [46], [47]. Besides these, wearable sensors are becoming increasingly popular [48].…”

Section: Introductionmentioning

confidence: 79%

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Burdack¹,

Giesselbach²,

Simak³

et al. 2023

Preprint

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<p>In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. 17 healthy adults (35.8 ± 11.1 years, 8 females, 9 males) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional CycleGAN, conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, it was tested whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.</p>

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Inter-limb differences in in-vivo tendon behavior, kinematics, kinetics and muscle activation during running

Cited by 3 publications

References 46 publications

Biarticular mechanisms of the gastrocnemii muscles enhance ankle mechanical power and work during running

Biarticular mechanisms of the gastrocnemii muscles enhance ankle mechanical power and work during running

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

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