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Abstract Muscle synergy analyses are used to enhance our understanding of motor control. Spatially fixed synergy weights coordinate multiple co-active muscles through activation commands, known as activation coefficients. To gain a more comprehensive understanding of motor learning, it is essential to understand how activation coefficients vary during a learning task and at different levels of movement proficiency. Participants walked on a line, a beam, and learned to walk on a tightrope—tasks that represent different levels of proficiency. Muscle synergies were extracted from electromyography signals across all conditions and the number of synergies was determined by the knee-point of the total variance accounted for (tVAF) curve. The results indicated that the tVAF of one synergy decreased with task proficiency, with the tightrope task resulting in the highest tVAF compared to the line and beam tasks. Furthermore, with increasing proficiency and after a learning process, trial-to-trial similarity increased and temporal overlap of synergy activation coefficients decreased. Consequently, we propose that precise adjustment and refinement of synergy activation coefficients play a pivotal role in motor learning.

Abstract A common theory of motor control posits that movement is controlled by muscle synergies. However, the behavior of these synergies during highly complex movements remains largely unexplored. Skateboarding is a hardly researched sport that requires rapid motor control to perform tricks. The objectives of this study were to investigate three key areas: (i) whether motor complexity differs between skateboard tricks, (ii) the inter-participant variability in synergies, and (iii) whether synergies are shared between different tricks. Electromyography data from eight muscles per leg were collected from seven experienced skateboarders performing three different tricks (Ollie, Kickflip, 360°-flip). Synergies were extracted using non-negative matrix factorization. The number of synergies (NoS) was determined using two criteria based on the total variance accounted for (tVAF > 90% and adding an additional synergy does not increase tVAF > 1%). In summary: (i) NoS and tVAF did not significantly differ between tricks, indicating similar motor complexity. (ii) High inter-participant variability exists across participants, potentially caused by the low number of constraints given to perform the tricks. (iii) Shared synergies were observed in every comparison of two tricks. Furthermore, each participant exhibited at least one synergy vector, which corresponds to the fundamental ‘jumping’ task, that was shared through all three tricks.

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

Introduction & Purpose Bones adapt its shape and strength in response to mechanical loading. Tissue-level mechanical stresses, i.e., hydrostatic compressive stress and octahedral shear stress, regulate biological events on the cellular and molecular level, and thereby impact tissue histomorphology and changes in bony geometry (Carter & Beaupré, 2007). Mechanobiological multi-scale simulations based on gait analysis data, musculoskeletal (MSK) modelling and finite element (FE) analysis enable to estimate tissue-level bone loads and predict growth patterns (Shefelbine & Carter, 2004). Such simulations have the potential to inform clinical decision-making in patients with pathological bone deformations, e.g. increased femoral anteversion angle (AVA), which are common in children with cerebral palsy (CP). However, the time-consuming process of creating subject-specific models has previously restricted studies to small sample sizes (n = 1-4 participants), thereby limiting the generalizability of findings (Carriero et al., 2011; Yadav et al., 2021). Moreover, the simulations have not been validated directly due to a lack of longitudinal experimental data, which hinders the widespread use and implementation in clinical settings. Over the last years, we focused our research efforts to overcome these hurdles. We developed a workflow to streamline the process to create subject-specific FE model (Koller et al., 2023). In this study we aimed to use this workflow and 1) analyze growth patterns in a comparable large sample of typically developing (TD) children and children with CP and 2) validate the growth predictions, i.e. compare predicted with measured bone growth. Methods Magnetic resonance images (MRI) and three-dimensional motion capture data including marker trajectories and ground reaction forces of 13 TD children (10 ± 2.2 years old, mass: 36.8 ± 9.5 kg) and 12 children with CP (10.4 ± 3.8 years old, mass: 30.1 ± 10.8 kg) were analyzed for this study. All participants walked without walking aids and with a self-selected speed. Details of the data collection are described in previous publications (Kainz et al., 2017; Koller et al., 2023). From 10 TD children we collected the MRI and motion capture data twice with approximately two years between occasions. MRI images were used to create subject-specific MSK models (Veerkamp et al., 2021) based on an OpenSim model (Kaneda et al., 2023), which allows to estimate medial and lateral knee joint contact forces (JCF) as well as the patellofemoral JCF. Each participant’s model and the corresponding gait analysis data were used to calculate joint angles, muscle forces and JCFs using inverse kinematics, static optimization and joint reaction load analyses with OpenSim 4.2, respectively. For each participant, muscle forces and JCF of two representative steps (left and right) were chosen as loading condition for the FE simulations and each side was analyzed separately. Each femur was segmented using 3D Slicer and the geometry was used to calculate the bending of the femoral shaft. Our previously developed tool was used to create subject-specific hexahedral meshes with several layers of elements aligned with the growth plates (Koller et al., 2023). Nine load instances were selected based on the JCF peaks and the valley in-between during the stance phase. JCFs and muscle forces (n = 26) acting on the femur at these timepoints were used as loading conditions for FE analysis. Linear elastic material properties were assigned to the different parts of the femur and FEBio3 was used to calculate principal stresses within the growth plates. The growth rate due to mechanical loading was estimated as the osteogenic index (OI), which was calculated based on the hydrostatic compressive stress and octahedral shear stress for each element within the growth plates (Stevens et al., 1999; Yadav et al., 2021). Positive and negative OI values indicate regions where growth is likely to be promoted or inhibited, respectively. Image comparison methods were employed to identify variability between OIs (Bradski, 2000). The knee flexion angle alters the orientation of knee JCFs in respect to the distal growth plate resulting in different induced stress regimes. Hence, we divided the children with CP in two groups: one with normal knee flexion angles (n = 15 femurs, CPnormal) and one with increased knee flexion (n = 8 femurs, CPhigh_knee_flexion) during the stance phase. For aim 1, we compared the femoral geometry and OI between CP groups and the TD children. For aim 2 (validation), growth predictions were performed for all femurs where data of two occasions was available (n = 20 femurs). Monte-Carlo-analysis (n = 1,320 for each femur) were performed to estimate which mechanobiological model parameters lead to the most realistic simulations. Linear regression analyses were used to identify whether measured development of AVA can be predicted by the mechanobiological multi-scale simulations. Results The bending of the femoral shaft was significantly different between all groups (Figure 1B). The representative reference OI distribution from the TD femurs showed a ring-shape at the proximal growth plate. Within the CP cohort, image comparison revealed higher inter-subject variability (p < 0.001) whereas some participants show ring-shape distribution similar to the TD cohort and others linear-gradient shapes with high values on the lateral side of the growth plate. At the distal growth plate, OI distribution for TD femurs showed highest values in the posterior notch region and on the medial-anterior edge. The OI distribution of the CPnormal group had its peak values in similar areas as the TD group but with additional peaks at the anterior-lateral edge. The OI distribution generated from the CPhigh_knee_flexion group showed a linear gradient from high to low values from anterior to posterior side (Figure 1D). Between data collection sessions, TD children grew 13.5 ± 3.3 cm and gained 8.2 ± 3.2 kg of body mass during the two years. Participants’ AVA changed between -13.1° and 11.8° (mean: -1.3 ± 5.8°) between sessions. Analysis of kinematics, muscle and JCFs did not show differences between those experiencing an increase of AVA to those that showed a decrease. Multi-scale predictions and measurements of AVA development showed significant linear correlation (p < 0.001) with an explanatory power of R2 = 0.5 (Figure 1C). Discussion At the distal growth plate, the OI distribution showed more growth in the anterior compared to the posterior region in the CPhigh_knee_flexion compared to the CPnormal and TD groups. Progressive promoted growth in the anterior compartment over a longer period could lead to higher anterior bending of the femoral shaft. Indeed, our geometrical analysis revealed a more bended femoral shaft in the CPhigh_knee_flexion group compared to other groups. The variability of the OI at the proximal growth plate within the CP cohort was higher compared to the TD group indicating that some CP participants are likely to experience normal growth whereas in others, the proximal femur will develop pathologically. This agrees with clinical observation where some children with CP develop deformities while others don’t. Despite the fact that no significant differences were found in joint kinematics, femoral loading and growth plate orientation between TD children with different growth patterns, multi-scale simulations were sensitive enough to identify differences and predict AVA development with reasonable accuracy. Conclusion Femoral growth is influenced by a complex interplay between gait pattern, femoral morphology and internal loading on a tissue-level. Our results showed that multi-scale simulations are able to discriminate between different growth patterns and predict growth trends in agreement with experimental observations. In order to increase our confidence in the simulations and pave the road to in-silico informed clinical decision-making in the near future, longitudinal simulation studies including a larger sample size and individuals with pathological growth are needed to be conducted. References Bradski, G. (2000). The OpenCV library. Dr. Dobb’s Journal of Software Tools. Carriero, A., Jonkers, I., & Shefelbine, S. J. (2011). Mechanobiological prediction of proximal femoral deformities in children with cerebral palsy. Computer Methods in Biomechanics and Biomedical Engineering, 14(3), 253–262. https://doi.org/10.1080/10255841003682505 Carter, D. R., & Beaupré, G. S. (2007). Skeletal function and form: Mechanobiology of skeletal development, aging, and regeneration. Cambridge University Press. Kainz, H., Hoang, H. X., Stockton, C., Boyd, R. R., Lloyd, D. G., & Carty, C. P. (2017). Accuracy and reliability of marker-based approaches to scale the pelvis, thigh, and shank segments in musculoskeletal models. Journal of Applied Biomechanics, 33(5), 354–360. https://doi.org/10.1123/jab.2016-0282 Kaneda, J. M., Seagers, K. A., Uhlrich, S. D., Kolesar, J. A., Thomas, K. A., & Delp, S. L. (2023). Can static optimization detect changes in peak medial knee contact forces induced by gait modifications? Journal of Biomechanics, 152, Article 111569. https://doi.org/10.1016/j.jbiomech.2023.111569 Koller, W., Gonçalves, B., Baca, A., & Kainz, H. (2023). Intra- and inter-subject variability of femoral growth plate stresses in typically developing children and children with cerebral palsy. Frontiers in Bioengineering and Biotechnology, 11, Article 1140527. https://doi.org/10.3389/fbioe.2023.1140527 Shefelbine, S. J., & Carter, D. R. (2004). Mechanobiological predictions of femoral anteversion in cerebral palsy. Annals of Biomedical Engineering, 32(2), 297–305. https://doi.org/10.1023/B:ABME.0000012750.73170.ba Stevens, S. S., Beaupré, G. S., & Carter, D. R. (1999). Computer model of endochondral growth and ossification in long bones: Biological and mechanobiological influences. Journal of Orthopaedic Research, 17(5), 646–653. https://doi.org/10.1002/jor.1100170505 Veerkamp, K., Kainz, H., Killen, B. A., Jónasdóttir, H., & van der Krogt, M. M. (2021). Torsion Tool: An automated tool for personalising femoral and tibial geometries in OpenSim musculoskeletal models. Journal of Biomechanics, 125, Article 110589. https://doi.org/10.1016/j.jbiomech.2021.110589 Yadav, P., Fernández, M. P., & Gutierrez-Farewik, E. M. (2021). Influence of loading direction due to physical activity on proximal femoral growth tendency. Medical Engineering & Physics, 90, 83-91. https://doi.org/10.1016/j.medengphy.2021.02.008

Abstract Background Accurate measurements of muscular performance are important for diagnostics, for example during rehabilitation after traumatic injuries but also in competitive sports. For these purposes, dynamometric devices are widely used and considered the gold standard for muscle strength testing. However, few previous studies have tested the reproducibility of peak moment (PM) at velocities close to the maximum device capability, and in general, reproducibility results cannot be transferred to other devices or test protocols. The purpose of this study was to evaluate the reproducibility of PM for different isometric and isokinetic knee extension exercises using the IsoMed 2000. Methods Thirty subjects volunteered in three repeated test sessions, including isometric knee extension (100° and 140° knee angle) and isokinetic knee extension (30°/s and 400°/s). Statistical analysis for comparison of sessions two and three included paired sample t-test, calculation of intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Additionally, Bland Altman statistics and corresponding plots were created. Results A significant difference between sessions in PM was found for isometric knee extension in one leg (140° left). Reproducibility was high for all conditions with ICC ranging from 0.964 to 0.988 and SEM in the range of 7.6 to 10.5 Nm. Bland Altman statistics revealed a bias between − 7.3 and 0.7 Nm. Conclusions Reproducibility of PM using the IsoMed 2000 was good after an initial familiarization trial with high values of relative reproducibility. Absolute reproducibility can be interpreted as appropriate for most common practical applications.

IntroductionCollagen peptide supplementation in conjunction with exercise has been shown to improve structural and functional adaptations of both muscles and the extracellular matrix. This study aimed to explore whether specific collagen peptide (SCP) supplementation combined with a concurrent training intervention can improve muscular stress after exercise-induced muscle damage, verified by reliable blood markers.Methods55 sedentary to moderately active males participating in a concurrent training (CT) intervention (3x/week) for 12 weeks were administered either 15 g of SCP or placebo (PLA) daily. Before (T1) and after the intervention (T2), 150 muscle-damaging drop jumps were performed. Blood samples were collected to measure creatine kinase (CK), lactate dehydrogenase (LDH), myoglobin (MYO) and high-sensitivity C-reactive protein (hsCRP) before, after, and at 2 h, 24 h and 48 h post exercise.ResultsA combination of concurrent training and SCP administration showed statistically significant interaction effects, implying a lower increase in the area under the curve (AUC) of MYO (p = 0.004, ηp2 = 0.184), CK (p = 0.01, ηp2 = 0.145) and LDH (p = 0.016, ηp2 = 0.133) in the SCP group. On closer examination, the absolute mean differences (ΔAUCs) showed statistical significance in MYO (p = 0.017, d = 0.771), CK (p = 0.039, d = 0.633) and LDH (p = 0.016, d = 0.764) by SCP supplementation.ConclusionIn conclusion, 12 weeks of 15 g SCP supplementation combined with CT intervention reduced acute markers of exercise-induced muscle damage and improved post-exercise regenerative capacity, as evidenced by the altered post-exercise time course. The current findings indicate that SCP supplementation had a positive effect on the early phase of muscular recovery by either improving the structural integrity of the muscle and extracellular matrix during the training period or by accelerating membrane and cytoskeletal protein repair.Clinical trial registrationhttps://www.clinicaltrials.gov/study/NCT05220371?cond=NCT05220371&rank=1, NCT05220371.

With recent technological advancements, quantitative analysis has become an increasingly important area within professional sports. However, the manual process of collecting data on relevant match events like passes, goals and tacklings comes with considerable costs and limited consistency across providers, affecting both research and practice. In football, while automatic detection of events from positional data of the players and the ball could alleviate these issues, it is not entirely clear what accuracy current state-of-the-art methods realistically achieve because there is a lack of high-quality validations on realistic and diverse data sets. This paper adds context to existing research by validating a two-step rule-based pass and shot detection algorithm on four different data sets using a comprehensive validation routine that accounts for the temporal, hierarchical and imbalanced nature of the task. Our evaluation shows that pass and shot detection performance is highly dependent on the specifics of the data set. In accordance with previous studies, we achieve F-scores of up to 0.92 for passes, but only when there is an inherent dependency between event and positional data. We find a significantly lower accuracy with F-scores of 0.71 for passes and 0.65 for shots if event and positional data are independent. This result, together with a critical evaluation of existing methodologies, suggests that the accuracy of current football event detection algorithms operating on positional data is currently overestimated. Further analysis reveals that the temporal extraction of passes and shots from positional data poses the main challenge for rule-based approaches. Our results further indicate that the classification of plays into shots and passes is a relatively straightforward task, achieving F-scores between 0.83 to 0.91 ro rule-based classifiers and up to 0.95 for machine learning classifiers. We show that there exist simple classifiers that accurately differentiate shots from passes in different data sets using a low number of human-understandable rules. Operating on basic spatial features, our classifiers provide a simple, objective event definition that can be used as a foundation for more reliable event-based match analysis.

IntroductionIt has been shown that short-term ingestion of collagen peptides improves markers related to muscular recovery following exercise-induced muscle damage. The objective of the present study was to investigate whether and to what extent a longer-term specific collagen peptide (SCP) supplementation combined with a training intervention influences recovery markers following eccentric exercise-induced muscle damage.MethodsFifty-five predominantly sedentary male participants were assigned to consume either 15 g SCP or placebo (PLA) and engage in a concurrent training (CT) intervention (30 min each of resistance and endurance training, 3x/week) for 12 weeks. Before (T1) and after the intervention (T2), eccentric muscle damage was induced by 150 drop jumps. Measurements of maximum voluntary contraction (MVC), rate of force development (RFD), peak RFD, countermovement jump height (CMJ), and muscle soreness (MS) were determined pre-exercise, immediately after exercise, and 24 and 48 h post-exercise. In addition, body composition, including fat mass (FM), fat-free mass (FFM), body cell mass (BCM) and extracellular mass (ECM) were determined at rest both before and after the 12-week intervention period.ResultsThree-way mixed ANOVA showed significant interaction effects in favor of the SCP group. MVC (p = 0.02, ηp2 = 0.11), RFD (p < 0.01, ηp2 = 0.18), peak RFD (p < 0.01, ηp2 = 0.15), and CMJ height (p = 0.046, ηp2 = 0.06) recovered significantly faster in the SCP group. No effects were found for muscle soreness (p = 0.66) and body composition (FM: p = 0.41, FFM: p = 0.56, BCM: p = 0.79, ECM: p = 0.58).ConclusionIn summary, the results show that combining specific collagen peptide supplementation (SCP) and concurrent training (CT) over a 12-week period significantly improved markers reflecting recovery, specifically in maximal, explosive, and reactive strength. It is hypothesized that prolonged intake of collagen peptides may support muscular adaptations by facilitating remodeling of the extracellular matrix. This, in turn, could enhance the generation of explosive force.Clinical trial registrationClinicalTrials.gov, identifier ID: NCT05220371.

IntroductionClimbing imposes substantial demands on the upper limbs and understanding the mechanical loads experienced by the joints during climbing movements is crucial for injury prevention and optimizing training protocols. This study aimed to quantify and compare upper limb joint loads and muscle activations during isometric finger hanging exercises with different arm lock-off positions.MethodsSeventeen recreational climbers performed six finger dead hangs with arm lock-offs at 90° and 135° of elbow flexion, as well as arms fully extended. Upper limb joint moments were calculated using personalized models in OpenSim, based on three-dimensional motion capture data and forces measured on an instrumented hang board. Muscle activations of upper limb muscles were recorded with surface electromyography electrodes.ResultsResults revealed that the shoulder exhibited higher flexion moments during arm lock-offs at 90° compared to full extension (p = 0.006). The adduction moment was higher at 135° and 90° compared to full extension (p

Little is known about the influence of mechanical loading on growth plate stresses and femoral growth. A multi-scale workflow based on musculoskeletal simulations and mechanobiological finite element (FE) analysis can be used to estimate growth plate loading and femoral growth trends. Personalizing the model in this workflow is time-consuming and therefore previous studies included small sample sizes (N < 4) or generic finite element models. The aim of this study was to develop a semi-automated toolbox to perform this workflow and to quantify intra-subject variability in growth plate stresses in 13 typically developing (TD) children and 12 children with cerebral palsy (CP). Additionally, we investigated the influence of the musculoskeletal model and the chosen material properties on the simulation results. Intra-subject variability in growth plate stresses was higher in cerebral palsy than in typically developing children. The highest osteogenic index (OI) was observed in the posterior region in 62% of the TD femurs while in children with CP the lateral region was the most common (50%). A representative reference osteogenic index distribution heatmap generated from data of 26 TD children’s femurs showed a ring shape with low values in the center region and high values at the border of the growth plate. Our simulation results can be used as reference values for further investigations. Furthermore, the code of the developed GP-Tool (“Growth Prediction-Tool”) is freely available on GitHub (https://github.com/WilliKoller/GP-Tool) to enable peers to conduct mechanobiological growth studies with larger sample sizes to improve our understanding of femoral growth and to support clinical decision making in the near future.

Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Itemsfor Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5). The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.

Gait asymmetry and skeletal deformities are common in many children with cerebral palsy (CP). Changes of the hip joint loading, i.e. hip joint contact force (HJCF), can lead to pathological femoral growth. A child's gait pattern and femoral morphology affect HJCFs. The twofold aim of this study was to (1) evaluate if the asymmetry in HJCFs is higher in children with CP compared to typically developing (TD) children and (2) identify if the bony morphology or the subject-specific gait pattern is the main contributor to asymmetric HJCFs. Magnetic resonance images (MRI) and three-dimensional gait analysis data of twelve children with CP and fifteen TD children were used to create subject-specific musculoskeletal models and calculate HJCF using OpenSim. Root-mean-square-differences between left and right HJCF magnitude and orientation were computed and compared between participant groups (CP versus TD). Additionally, the influence on HJCF asymmetries solely due to the femoral morphology and solely due to the gait pattern was quantified. Our findings demonstrate that the gait pattern is the main contributor to asymmetric HJCFs in CP and TD children. Children with CP have higher HJCF asymmetries which is probably the result of larger asymmetries in their gait pattern compared to TD children. The gained insights from our study highlight that clinical interventions should focus on normalizing the gait pattern and therefore the hip joint loading to avoid the development of femoral deformities.

Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate's drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players' capability with respect to this quality-especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile-benefiting the participants and the spectators alike.

Abstract Stretch-induced residual force enhancement (rFE) is associated with increased performance in a stretch–shortening cycle (SSC). Although the influence of different range of motions and muscle–tendon unit lengths has been investigated in pure stretch-hold experiments in vivo, the contribution to a SSC movement in human muscles remains unclear. In two sessions, 25 healthy participants performed isometric reference (ISO), shortening hold (SHO) and SSC contractions on an isokinetic dynamometer. We measured the net knee-joint torque, rotational mechanical work, knee kinematics and fascicle behavior (m. vastus lateralis) of the upper right leg. In session 1 the SHO- and SSC-magnitude was changed respectively (SHO: 50°–20°, 80°–20° and 110°–20°; SSC: 20°–50°–20°, 20°–80°–20° and 20°–110°–20°) and in session 2 the muscle–tendon unit length (SHO: 50°–20°, 80°–50° and 110°–80°; SSC: 20°–50°–20°, 50°–80°–50° and 80°–110°–80°; straight leg = 0°). In both sessions, rotational work was significantly (p

The aim of this study was to determine the differences in golf swing execution in terms of the parameters of the pelvis and thorax movement between the sexes in junior golfers and their relation to the golf club velocity. Elite female and male players (age: 15.4 ± 1.0 and 15.8 ± 1.7 years, respectively) performed 10 golf swings with a driver under laboratory conditions. Pelvis and thorax movement parameters and golf club velocities were measured using a three-dimensional motion capture system. Statistical parametric mapping analysis of pelvis–thorax coupling revealed a significant difference (p < 0.05) between boys and girls during backswing. Analysis of variance showed a significant effect of sex on the parameters of maximal pelvic rotation (F = 6.28, p = 0.02), X-factor (F = 5.41, p = 0.03), and golf club velocity (F = 31.98, p < 0.01). No significant relationship was found between pelvis and thorax movement parameters and golf club velocity in the girls. We found a significant negative relationship between the parameters of maximal thorax rotation and golf club velocity (r = −0.941, p < 0.01) and between X-Factor and golf club velocity (r = −0.847, p < 0.05) in the boys. We suggest that these negative relationships in males were caused by the influence of hormones during their maturation and biological development, where there is decreased flexibility (lower shoulders rotation and X-factor) and growth of muscle strength (higher club head velocity).

The use of small, interconnected and intelligent tools within the broad framework of pervasive computing for analysis and assessments in sport and physical activity is not a trend in itself but defines a way for information to be handled, processed and utilised: everywhere, at any time. The demand for objective data to support decision making prompted the adoption of wearables that evolve to fulfil the aims of assessing athletes and practitioners as closely as possible with their performance environments. In the present paper, we mention and discuss the advancements in ubiquitous computing in sports and physical activity in the past 5 years. Thus, recent developments in wearable sensors, cloud computing and artificial intelligence tools have been the pillars for a major change in the ways sport-related analyses are performed. The focus of our analysis is wearable technology, computer vision solutions for markerless tracking and their major contribution to the process of acquiring more representative data from uninhibited actions in realistic ecological conditions. We selected relevant literature on the applications of such approaches in various areas of sports and physical activity while outlining some limitations of the present-day data acquisition and data processing practices and the resulting sensors’ functionalities, as well as the limitations to the data-driven informed decision making in the current technological and scientific framework. Finally, we hypothesise that a continuous merger of measurement, processing and analysis will lead to the development of more reliable models utilising the advantages of open computing and unrestricted data access and allow for the development of personalised-medicine-type approaches to sport training and performance.

We applied social networks analysis to objectively discriminate and describe interpersonal interaction dynamics of players across different top-coaching styles. The aim was to compare metrics in the passing networks of Jürgen Klopp, Pep Guardiola, and Mauricio Pochettino across the UEFA Champions League seasons from 2017 to 2020. Data on completed passes from 92 games were gathered and average passing networks metrics were computed. We were not only able to find the foundations on which these elite coaches build the passing dynamics in their respective teams, but also to determine important differences that represent their particular coaching signatures. The local cluster coefficient was the only metric not significantly different between coaches. Still, we found higher average shortest-path length for Guardiola's network (mean ± std = 3.00 ± 0.45 a.u.) compared to Klopp's (2.80 ± 0.52 a.u., p = 0.04) and Pochettino's (2.70 ± 0.39 a.u., p = 0.01). Density was higher for Guardiola's (64.16 ± 20.27 a.u.) than for Pochettino's team (51.42 ± 17.28 a.u., p = 0.008). The largest eigenvalue for Guardiola's team (65.95 ± 16.79 a.u.) was higher than for Klopp's (47.06 ± 17.25 a.u., p < 0.001) and Pochettino's (42,62 ± 12.01 a.u., p < 0.001). Centrality dispersion was also higher for Guardiola (0.14 ± 0.02 a.u.) when compared to Klopp (0.12 ± 0.03 a.u., p = 0.008). The local cluster coefficient seems to build the foundation for passing work, however, cohesion characteristics among players in the three teams of the top coaches seems to characterize their own footprint regarding passing dynamics. Guardiola stands out by the high number of passes and the enhanced connection of the most important players in the network. Klopp and Pochettino showed important similarities, which are associated to preferences toward more flexibility of interpersonal linkages synergies.

Studies indicate that injury risks in tennis depend on the playing surface type. In order to assess loading during tennis specific movements, plantar pressure parameters are determined and analyzed. So far, only comparisons between whole stride sequences on different surfaces have been performed showing some inconsistent results. We assumed that on the more slippery clay higher vertical forces are required to accelerate, and that on hard-court higher loadings occur during deceleration. Hence, we analyzed the influence of the playing surface on respective types of steps. Eight experienced male tennis players performed two different tennis specific movements on clay and hard-court. We used a Pedar-X insole measurement system for determining selected plantar pressure parameters for the whole foot as well as for the forefoot and rear foot area. Steps were categorized as accelerating or decelerating regarding the path of the center of pressure during impact of the foot on the ground. For accelerating steps, a multivariate analysis revealed significant differences (Pillai-Spur; p < .05) for both repeated factors as well as their interaction for both playing conditions. All loading parameters were significantly higher in the forefoot area on clay for one of the two playing conditions investigated. For decelerating steps, the multivariate analysis revealed significant differences for both repeated factors for one playing condition. Higher values were observed for all loading parameters in the rear foot area in both playing conditions on clay. Running styles during tennis specific movements depend on the court surface. Separate analyses of acceleration and deceleration steps may help revealing high-risk parts and periods.

The aim of this study was to determine and analyze the differences between the players’ internal (IL) and external load (EL) in different time zones (T1: 0–45 min, T2: 45–70 min, T3: 0–70 min, T4: 70–90 min, T5: 45–90 min, and T6: 0–90 min) in elite youth soccer matches (U17–19 age category). The monitored group comprised elite youth soccer players (n = 66; age = 17.5 ± 1.2 years; body height = 178.5 ± 8.7 cm; body mass = 70.4 ± 6.3 kg). Multivariate analysis of variances was used to compare the following variables: relative total distance covered (TDCrel), distance covered in different speed zones (Z1–Z6), high-metabolic load distance (HMLD), maximum running speed (Smax), number of acceleration (ACC) and deceleration (DCC) entries in different speed zones (Z1, Z2, Z3), and maximum heart rate (HRmax). Results revealed significant differences (p < 0.05) in the first 70 min (T3) compared to the last 20 min of the match (T4) in the following: TDCrel was higher, up to 6.6% (123.09 ± 9.48 vs. 115.03 ± 9.42 m.min−1); distance in Z5 (22.4%, 6.08 ± 1.82 vs. 4.72 ± 1.72 m.min−1); Z4 (18.6%, 20.15 ± 4.82 vs. 16.40 ± 3.48 m.min−1); Z3 (10.4%, 53.06 ± 47.52 m.min−1); and HMLD (16.1%, 34.86 ± 5.67 vs. 29.26 ± 5.11 m.min−1). We also found higher running performance in the first half (T1) than in the second half (T5) in the following parameters: TDCrel, Z5, Z4, ACCZ1, ACCZ2, and DCCZ1. With progressive time (in T4) we found a significant decrease in physical running performance, probably due to fatigue, which can lead to potential injury or losing in a match. The results of this study may provide helpful information in developing training strategies for coaches and in the set-up of plan for potential substitution of exhausted players mainly for the last 20 min of a match (T4). The present results are expressed as a relative value and should be compared with other study results with irregular timing zones.

In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.

This study investigated the activity profile during small-sided games (SSG) in elite youth soccer players. Internal load (IL) including heart rate (HR) and external load (EL) such as distance covered in different speed-zones (SZ) were collected from forty-eight players of three different teams (U15, U16, U18). The investigation included a total of eighteen 5vs.5 SSGs, each consisting of four 2-minute bouts on a 40x32m pitch during spring season. Total group results (n = 48) showed a reduction in total-distance (p = 0.001; [Formula: see text] = 0.12), high-intensity-running (p = 0.009; [Formula: see text] = 0.09), and low-intensity-running distance (p = 0.028; [Formula: see text] = 0.07) between bouts. Similarly, a reduction in the number of both acceleration-low (p = 0.001; [Formula: see text] = 0.12) and deceleration-high (p = 0.003; [Formula: see text] = 0.11) values was observed. Additionally, time spent in HR-zones 3 and 4 (p≤0.007; [Formula: see text] ≥ 0.10), increased, with a reduction in HR-zone 1 (p = 0.000, [Formula: see text] = 0.25). Age group comparison showed less distance covered in SZ 1 (p≤0.000; [Formula: see text] = 0.56) and greater deceleration-high values (p≤0.038; [Formula: see text] = 0.32) in U15 players compared to other age groups. Further, U15 showed lower values in low-intensity-running compared to U18 (p = 0.038; [Formula: see text] = 0.22). No age-related differences were found for IL and repeated sprint ability (RSA) values. The higher EL in younger age groups should be taken into account when implementing soccer specific SSGs. In addition, HRmean values between 80-85% of HRmax and RSA numbers, which are similar to match-play data, indicate SSGs as an effective training tool to prepare youth soccer athletes for the demands of competition.

In everyday muscle action or exercises, a stretch-shortening cycle (SSC) is performed under different levels of intensity. Thereby, compared to a pure shortening contraction, the shortening phase in a SSC shows increased force, work, and power. One mechanism to explain this performance enhancement in the SSC shortening phase is, besides others, referred to the phenomenon of stretch-induced increase in muscle force (known as residual force enhancement; rFE). It is unclear to what extent the intensity of muscle action influences the contribution of rFE to the SSC performance enhancement. Therefore, we examined the knee torque, knee kinematics, m. vastus lateralis fascicle length, and pennation angle changes of 30 healthy adults during isometric, shortening (CON) and stretch-shortening (SSC) conditions of the quadriceps femoris. We conducted maximal voluntary contractions (MVC) and submaximal electrically stimulated contractions at 20%, 35%, and 50% of MVC. Isometric trials were performed at 20° knee flexion (straight leg: 0°), and dynamic trials followed dynamometer-driven ramp profiles of 80°–20° (CON) and 20°–80°–20° (SSC), at an angular velocity set to 60°/s. Joint mechanical work during shortening was significantly (p < 0.05) enhanced by up to 21% for all SSC conditions compared to pure CON contractions at the same intensity. Regarding the steady-state torque after the dynamic phase, we found significant torque depression for all submaximal SSCs compared to the isometric reference contractions. There was no difference in the steady-state torque after the shortening phases between CON and SSC conditions at all submaximal intensities, indicating no stretch-induced rFE that persisted throughout the shortening. In contrast, during MVC efforts, the steady-state torque after SSC was significantly less depressed compared to the steady-state torque after the CON condition (p = 0.034), without significant differences in the m. vastus lateralis fascicle length and pennation angle. From these results, we concluded that the contribution of the potential enhancing factors in SSCs of the m. quadriceps femoris is dependent on the contraction intensity and the type of activation.

Background: Novel player tracking technologies can change the understanding of performance determinants in team sports by allowing to accurately measuring the activity demands. The aim of this study was to identify how the defensive pressure affects the time-motion variables and the technical actions in basketball. Methods: Twenty international male players (age: 16.05 ± 2.09 years, weight: 73.13 ± 8.10 kg, height: 183.10 ± 5.88 cm) played two 10 min basketball quarters, where they used a man-to-man 1/4-court defense until the 4th min (F1/4), changed to man-to-man full court (FULL) for 3 min and, from the 7th to the 10th min returned to 1/4-court defense (S1/4). A computerized notational analysis was performed using Simi Scout and positional data were captured with the Ubisense Real Time Location System (mean sampling rate 3.74 ± 0.45 Hz per transmitter/player). Results: The time-motion variables presented similar results between defensive conditions, showing a total distance covered around 90 m/min. However, results suggested possible vertical jump impairments in S1/4 periods. There was more distance covered while jogging in the offensive court (38.15 ± 12.17 m/min offensive court vs. 32.94 ± 10.84 m/min defensive court, p

Abstract Background Prolonged sitting is ubiquitous in modern society and linked to several diseases. Height-adjustable desks are being used to decrease worksite based sitting time (ST). Single-desk sit-to-stand workplaces exhibit small ST reduction potential and short-term loss in performance. The aim of this paper is to report the study design and methodology of an ACTIVE OFFICE trial. Design The study was a 1-year three-arm, randomized controlled trial in 18 healthy Austrian office workers. Allocation was done via a regional health insurance, with data collection during Jan 2014 – March 2015. Participants were allocated to either an intervention or control group. Intervention group subjects were provided with traditional or two-desk sit-to-stand workstations in either the first or the second half of the study, while control subjects did not experience any changes during the whole study duration. Sitting time and physical activity (IPAQ-long), cognitive performance (text editing task, Stroop-test, d2R test of attention), workload perception (NASA-TLX) and physiological parameters (salivary cortisol, heartrate variability and body weight) were measured pre- and post-intervention (23 weeks after baseline) for intervention and control periods. Postural changes and sitting/standing time (software logger) were recorded at the workplace for the whole intervention period. Discussion This study evaluates the effects of a novel two-desk sit-to-stand workplace on sitting time, physical parameters and work performance of healthy office based workers. If the intervention proves effective, it has a great potential to be implemented in regular workplaces to reduce diseases related to prolonged sitting. Trial registration ClinicalTrials.gov Identifier: NCT02825303 , July 2016 (retrospectively registered).

The study aimed to evaluate the test-retest reliability of a newly developed 356 Soccer Shooting Test (356-SST), and the discriminative ability of this test with respect to the soccer players' proficiency level and leg dominance. Sixty-six male soccer players, divided into three groups based on their proficiency level (amateur, n = 24; novice semi-professional, n = 18; and experienced semi-professional players, n = 24), performed 10 kicks following a two-step run up. Forty-eight of them repeated the test on a separate day. The following shooting variables were derived: ball velocity (BV; measured via radar gun), shooting accuracy (SA; average distance from the ball-entry point to the goal centre), and shooting quality (SQ; shooting accuracy divided by the time elapsed from hitting the ball to the point of entry). No systematic bias was evident in the selected shooting variables (SA: 1.98±0.65 vs. 2.00±0.63 m; BV: 24.6±2.3 vs. 24.5±1.9 m s-1; SQ: 2.92±1.0 vs. 2.93±1.0 m s-1; all p>0.05). The intra-class correlation coefficients were high (ICC = 0.70-0.88), and the coefficients of variation were low (CV = 5.3-5.4%). Finally, all three 356-SST variables identify, with adequate sensitivity, differences in soccer shooting ability with respect to the players' proficiency and leg dominance. The results suggest that the 356-SST is a reliable and sensitive test of specific shooting ability in men's soccer. Future studies should test the validity of these findings in a fatigued state, as well as in other populations.

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications.

A prototype system for monitoring, transmitting and processing performance data in sports for the purpose of providing feedback has been developed. During training, athletes are equipped with a mobile device and wireless sensors using the ANT protocol in order to acquire biomechanical, physiological and other sports specific parameters. The measured data is buffered locally and forwarded via the Internet to a server. The server provides experts (coaches, biomechanists, sports medicine specialists etc.) with remote data access, analysis and (partly automated) feedback routines. In this way, experts are able to analyze the athlete’s performance and return individual feedback messages from remote locations.