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Introduction & Purpose During training and competition, athletes must cope with numerous demands that can influence their performance. These include physiological and mental fatigue, psychological stress, and the ability to adapt quickly to varying situations. Controlling the high number of degrees of freedom with which the musculoskeletal system can produce movements is challenging for the central nervous system CNS (Bernstein, 1967), even without the context of a sporting setting. One common theory in the field of motor control posits the existence of muscle synergies (Turpin et al., 2021). These are constituted of synergy vectors within the spinal cord that comprise the relative weightings of different co-active muscles. Synergy vectors are activated by time-varying activation coefficients, which correspond to the central commands from supraspinal areas. Consequently, the CNS is not required to individually time and scale the activation of each muscle independently; rather, it only regulates the activation of a limited number of synergies (Turpin et al., 2021). This simplification strategy has been extensively studied in a variety of settings, including locomotion (Boccia et al., 2018; McGowan et al., 2010) and reaching movements (Scano et al., 2019). However, muscle synergies were rarely studied in sport-like settings which demands fall beyond daily tasks. In this light, we conducted several studies in order to build a deeper understanding of motor control in movements with high demands. This abstract presents a summary of the main findings of four studies. Specifically, SKATE, examining the complex movements of skateboard tricks; LEARN, demonstrating modifications as participants learned to walk on a tightrope; STRESS, investigating alterations under psychological stress during treadmill walking; and FATIGUE, exploring the fatigue strategies of climbers during overhead hanging tasks. The overarching aim of these studies was to enhance our understanding of motor control and adaptation in movements of a high-demand nature. Methods To study muscle synergies, surface electromyography signals from multiple muscles were recorded, filtered, rectified, amplitude- and time-normalised, and concatenated across captured trials. Subsequently, factorisation algorithms were employed to extract the spatial synergy vectors and time-varying activation coefficients for each participant. The required number of synergies to perform the movement was then determined by the total variance accounted for by a given number of synergies (Turpin et al., 2021). SKATE Seven recreational skateboarders performed three tricks (Ollie, Kickflip, 360°-flip), which involved different rotations of the board in the air. Each participant was required to land the Ollie, Kickflip and 360°-flip successfully six times. Muscle activity of eight muscles per leg was collected and used to extract muscle synergies of each trick during the take-off phase. The degree of similarity between synergy vectors among tricks was quantified by calculating the Pearson correlation coefficient. Synergies were identified as similar if the correlation exceeded a threshold of 0.623 (Kaufmann et al., 2024). LEARN Ten participants were required to walk over a line taped on the floor, a beam, and learn to walk over a tightrope, all within one single data collection session. Muscle activity of thirteen muscles of the right leg was collected for several trials of each walking task and used to extract muscle synergies. The temporal overlap between different synergies within one trial and the trial-to-trial similarity between different trials within one synergy were determined through the Pearson correlation of the activation coefficients (Figure 1A). STRESS Eight participants walked on a treadmill at a self-selected, constant velocity under two conditions in a random order. Once walking during a psychological stress condition, induced by the Paced Auditory Serial Addition Task (Gronwall, 1977), and once without stress. The stress level was measured through the tonic skin conductance, and force insoles were used to determine foot contacts. Muscle activity of four to seven muscles on each leg was collected for all steps and used to extract muscle synergies. The trial-to-trial similarity of the activation coefficients was determined by the Pearson correlation coefficient (Figure 1A). The coefficient of variation among steps was employed to quantify the trial-to-trial similarity of the participants’ stance-phase to gait-cycle ratio as a temporal-spatial gait parameter. FATIGUE Eleven climbers performed sustained isometric finger hangings until failure. Muscle activity of six arm and trunk muscles per limb was recorded throughout the hang and used to extract muscle synergies. Subsequently, similar synergy vectors among participants were clustered. The mean activation from the activation coefficients of synergies within the same cluster was compared between the first and last 20% of the hang in order to identify changes between a non-fatigued and a fatigued stage. Results SKATE Three to six synergies were required among participants and tricks. At least one similar synergy vector was identified for each pair of trick comparisons. Furthermore, one consistent synergy vector was observed in all three tricks. This synergy vector was primarily formed by the quadriceps muscles and was activated during the initial third of the take-off phase, thereby being responsible for the fundamental task of jumping. LEARN Across participants, four to eight synergies were required to complete all walking tasks. Statistical analyses revealed a significantly higher trial-to-trial similarity (p < 0.05) alongside a lower temporal overlap of activation coefficients during walking on a line compared to walking over the beam or the tightrope and during beam walking compared to tightrope walking. Furthermore, trial-to-trial similarity was higher and overlap was lower at the end of the learning process compared to the beginning. STRESS Three to seven synergies were required to walk on the treadmill. During the stress condition, participants exhibited higher stress levels (p < 0.01) and a lower trial-to-trial similarity of the stance-phase to gait-cycle ratio (p < 0.05). No significant differences were found regarding the trial-to-trial similarity of activation coefficients. FATIGUE Participants required two or three synergies to perform the hanging task. Consequently, synergies were identified into two clusters (Figure 1B). The first cluster, which was mainly comprised of forearm muscles, demonstrated no difference in the activation coefficient recruitment between the first and last 20% of the hang. In contrast, the second cluster, which was mainly comprised of more postural muscles such as the trapezius and biceps, exhibited a higher activation during the fatigued stage (p < 0.001). Discussion In light of the findings of the presented studies, three major points warrant discussion. Firstly, similar synergy vectors were present in movements with a high level of complexity, such as skateboard tricks. This result is similar to findings in daily tasks and supports the theory that movements with similar subtasks recruit similar synergy vectors (Boccia et al., 2018; Kaufmann et al., 2024; Scano et al., 2019). We propose that, if fundamental subtasks such as a jump are shared between movements, similar synergies are recruited, even in movements with high technical demands. Secondly, the fine-tuning of time-varying activation coefficients is a key factor for learning and to quickly adapt to changing demands. In particular, the trial-to-trial similarity of activation coefficients increased and temporal overlap decreased with increasing movement proficiency. Additionally, the LEARN study revealed that the amount of activation of different synergies varies in relation to the surface on which the participants walked (Kaufmann et al., 2023). Similarly, the amount of activation of a synergy formed by postural muscles changed throughout fatigue during overhead hangs. Consequently, these findings indicate that activation coefficients adapt to changing demands. This is also evidenced by a simulation study, which demonstrated that solely by scaling the magnitude of activation coefficients, walking was possible with changing mechanical demands, such as weight support, added weight, or added mass (McGowan et al., 2010). Thirdly, it was observed that psychological stress had an effect on temporal-spatial gait parameters, but not on synergies. One possible explanation for this is that even in situations without stress, activation coefficients are recruited flexible across trials. This flexibility is not further increased with the presence of psychological stress. In light of the optimal feedback control theory (Todorov & Jordan, 2002), the flexibility and fine-tuning of the activation coefficients can be elucidated as an update mechanism of the CNS based on permanent feedback, which enables adaptation to task demands and maintains task performance. Conclusion The results of these studies allow us to draw the following conclusions: fundamental subtasks share similar muscle synergies, and fine-tuning as well as flexibility of the temporal activation coefficients is crucial to adapt to varying demands. We demonstrated that sport-like settings can be used to enhance our understanding of human motor control. References Bernstein, N. (1967). The coordination and regulation of movements. Perganmon Press. Boccia, G., Zoppirolli, C., Bortolan, L., Schena, F., & Pellegrini, B. (2018). Shared and task‐specific muscle synergies of Nordic walking and conventional walking. Scandinavian Journal of Medicine & Science in Sports, 28(3), 905-918. https://doi.org/10.1111/sms.12992 Gronwall, D. M. (1977). Paced auditory serial-addition task: A measure of recovery from concussion. Perceptual and Motor Skills, 44(2), 367-373. https://doi.org/10.2466/pms.1977.44.2.367 Kaufmann, P., Koller, W., Wallnöfer, E., Goncalves, B., Baca, A., & Kainz, H. (2023). Trial-to-trial similarity and distinctness of muscle synergy activation coefficients increases during learning and with a higher level of movement proficiency. bioRxiv, 2023.2009.2019.558460. https://doi.org/10.1101/2023.09.19.558460 Kaufmann, P., Zweier, L., Baca, A., & Kainz, H. (2024). Muscle synergies are shared across fundamental subtasks in complex movements of skateboarding. Scientific Reports, 14, Article 12860. https://doi.org/10.1038/s41598-024-63640-5 McGowan, C. P., Neptune, R. R., Clark, D. J., & Kautz, S. A. (2010). Modular control of human walking: Adaptations to altered mechanical demands. Journal of Biomechanics, 43(3), 412-419. https://doi.org/10.1016/j.jbiomech.2009.10.009 Scano, A., Dardari, L., Molteni, F., Giberti, H., Tosatti, L. M., & d’Avella, A. (2019). A comprehensive spatial mapping of muscle synergies in highly variable upper-limb movements of healthy subjects. Frontiers in Physiology, 10, Article 1231. https://doi.org/10.3389/fphys.2019.01231 Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5, 1226-1235. https://doi.org/10.1038/nn963 Turpin, N. A., Uriac, S., & Dalleau, G. (2021). How to improve the muscle synergy analysis methodology? European Journal of Applied Physiology, 121, 1009-1025. https://doi.org/10.1007/s00421-021-04604-9 |
