Muscle synergies are shared across fundamental subtasks in complex movements of skateboarding.

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.

Event detection in football: Improving the reliability of match analysis.

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.

Reduction in systemic muscle stress markers after exercise-induced muscle damage following concurrent training and supplementation with specific collagen peptides - a randomized controlled trial.

Introduction: Collagen 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. Methods: 55 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. Results: A 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. Conclusion: In 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 registration: https://www.clinicaltrials.gov/study/NCT05220371?cond=NCT05220371&rank=1, NCT05220371.

Reproducibility of peak moment for isometric and isokinetic knee extension exercise.

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.

Neuromechanics of finger hangs with arm lock-offs: analyzing joint moments and muscle activations to improve practice guidelines for climbing.

Introduction: Climbing 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. Methods: Seventeen 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. Results: Results 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 < 0.001), as well as the rotation moments (p < 0.001). The elbows exhibited increasing flexion moments with the increase in the arm lock-off angle (p < 0.001). Muscle activations varied across conditions for biceps brachii (p < 0.001), trapezius (p < 0.001), and latissimus dorsi, except for the finger flexors (p = 0.15). Discussion: Our findings indicate that isometric finger dead hangs with arms fully extended are effective for training forearm force capacities while minimizing stress on the elbow and shoulder joints. These findings have important implications for injury prevention and optimizing training strategies in climbing.

Influence of specific collagen peptides and 12-week concurrent training on recovery-related biomechanical characteristics following exercise-induced muscle damage-A randomized controlled trial.

Introduction: It 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. Methods: Fifty-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. Results: Three-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). Conclusion: In 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 registration: ClinicalTrials.gov, identifier ID: NCT05220371.

The gait pattern and not the femoral morphology is the main contributor to asymmetric hip joint loading.

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.

Robust deep learning-based gait event detection across various pathologies.

The correct estimation of gait events is essential for the interpretation and calculation of 3D gait analysis (3DGA) data. Depending on the severity of the underlying pathology and the availability of force plates, gait events can be set either manually by trained clinicians or detected by automated event detection algorithms. The downside of manually estimated events is the tedious and time-intensive work which leads to subjective assessments. For automated event detection algorithms, the drawback is, that there is no standardized method available. Algorithms show varying robustness and accuracy on different pathologies and are often dependent on setup or pathology-specific thresholds. In this paper, we aim at closing this gap by introducing a novel deep learning-based gait event detection algorithm called IntellEvent, which shows to be accurate and robust across multiple pathologies. For this study, we utilized a retrospective clinical 3DGA dataset of 1211 patients with four different pathologies (malrotation deformities of the lower limbs, club foot, infantile cerebral palsy (ICP), and ICP with only drop foot characteristics) and 61 healthy controls. We propose a recurrent neural network architecture based on long-short term memory (LSTM) and trained it with 3D position and velocity information to predict initial contact (IC) and foot off (FO) events. We compared IntellEvent to a state-of-the-art heuristic approach and a machine learning method called DeepEvent. IntellEvent outperforms both methods and detects IC events on average within 5.4 ms and FO events within 11.3 ms with a detection rate of ≥ 99% and ≥ 95%, respectively. Our investigation on generalizability across laboratories suggests that models trained on data from a different laboratory need to be applied with care due to setup variations or differences in capturing frequencies.

Intra- and inter-subject variability of femoral growth plate stresses in typically developing children and children with cerebral palsy.

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.

Do the Pelvic and Thorax Movements Differ between the Sexes and Influence Golf Club Velocity in Junior Golfers?

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).

Modeling the formation of defensive gaps in basketball: Cutting on a teammate's drive.

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.

Machine learning application in soccer: a systematic review.

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 Items for 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.

A kinematic model for assessment of hip joint range-of-motion in fast sport movements using spreading angles.

The work investigates the relationship between the leg spread and hip joint flexibility during kicking action of highly skilled martial artists. Vector-based spreading angles (VSAs), as a simplified way to assess kick execution patterns and movement ranges, are proposed. The first VSA measures the angle between the two femur vectors (i.e., upper leg spread), whereas the second describes the full-leg spread. The proposed measures were applied to 3D motion analysis performed on 33 participants at various skill levels who executed double side kicks and performed two static flexibility tests (hip joint flexion and abduction). Statistical analysis showed that the proposed parameters differentiate between skill levels. Both VSAs and VSA rate of change showed high Pearson correlation to the quality of execution (the average of 10 subjective kickboxing experts' evaluation scores) at critical instants of the kick execution , i.e., during the first leg elevation and kick (angles: ρ > 0.8; p < 0.001). In addition, they significantly correlated to maximum static hip abduction at any instant (ρ = 0.37-0.69; p < 0.05). The work established that both static and dynamic joint flexibility contribute to high-performance levels and could be used for initial selection and assessment of training effectiveness.

Modelling the relationship between load and repetitions to failure in resistance training: A Bayesian analysis.

To identify the relationship between load and the number of repetitions performed to momentary failure in the pin press exercise, the present study compared different statistical model types and structures using a Bayesian approach. Thirty resistance-trained men and women were tested on two separate occasions. During the first visit, participants underwent assessment of their one-repetition maximum (1-RM) in the pin press exercise. On the second visit, they performed sets to momentary failure at 90%, 80% and 70% of their 1-RM in a fixed order during a single session. The relationship between relative load and repetitions performed to failure was fitted using linear regression, exponential regression and the critical load model. Each model was fitted according to the Bayesian framework in two ways: using an across-subjects pooled data structure and using a multilevel structure. Models were compared based on the variance explained (R2) and leave-one-out cross-validation information criterion (LOOIC). Multilevel models, which incorporate higher-level commonalities into individual relationships, demonstrated a substantially better fit (R2: 0.97-0.98) and better predictive accuracy compared to generalised pooled-data models (R2: 0.89-0.93). The multilevel 2-parameter exponential regression emerged as the best representation of data in terms of model fit, predictive accuracy and model simplicity. The relationship between load and repetitions performed to failure follows an individually expressed exponential trend in the pin press exercise. To accurately predict the load that is associated with a certain repetition maximum, the relationship should therefore be modelled on a subject-specific level.

Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments.

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.

The Effects of Visual Feedback on Performance in Heart Rate- and Power-Based-Tasks during a Constant Load Cycling Test.

Performance feedback can be essential for cyclists to help with pacing their efforts during competitions and also during standardized performance tests. However, the choice of feedback options on modern bike computers is limited. Moreover, little research on the effectiveness of the currently used feedback methods is available. In this study, two novel feedback variants using a bar or a tacho to visualize targets and deviation from targets were compared to a classic design using only numbers. Participants (6 female and 25 male trained to well-trained athletes) completed a protocol consisting of three heart rate-based tasks and one power-based task. The displays were compared with respect to their ability to guide athletes during their trials. Results showed lower root mean square error (RMSE) of the novel variants, but no significant effect of feedback variant on RMSE was found for both tasks (p > 0.05). However, when comparing the feedback variants on a person to person basis, significant differences were found for all investigated scenarios (p < 0.001). This leads to the conclusion that novel feedback variants can improve athletes' ability to follow heart rate-based and power-based protocols, but even better results might be achieved by individualizing the feedback.

Guardiola, Klopp, and Pochettino: The Purveyors of What? The Use of Passing Network Analysis to Identify and Compare Coaching Styles in Professional Football.

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.

Preventive strategy of flatfoot deformity using fully automated procedure.

A non-invasive, no radiation, out-of-hospital automated system is proposed to identify low arch integrated in the design and manufacturing of personalized orthoses using parametric modelling. The aim of the design process is to integrate assistive technology with assessment and prevent low arch progressing to a more serious case - flatfoot. In the automated procedure, we developed an assessment method including reliable thresholds of foot type classification and test protocol to reduce interferences due to preceding activities, an automation to translate scanned data into parametric design for orthotic customization, finite element model evaluating effectiveness of the personalized design, and a personalized comparative test to evaluate the long-term improvement of foot arch shape. Our low arch threshold established by subject-specific 3D models reduced the misclassification rate from 55%, as previously reported to 6.9%. Individuals who engaged in sedentary activity (i.e. sitting) had the greater change in arch height compared to active activity (i.e. standing and walking), which is more likely to affect the obtained measure. Therefore, a test protocol now states that participants are not allowed to sit over 100 min prior the measurement to reduce such interference. We have proposed and tested an automated algorithm to translate scanned data including seven foot's parameters into customised parametric design of the insert. The method decreases the required time of orthotic computer-aided design from over 3 h to less than 2 min. A finite element analysis procedure was additionally developed to assess the performance of geometries and material of designed orthotic based on the distribution of plantar pressure and internal stress. The personalized comparative assessment based on midfoot contact area was carried out periodically for follow-up and the orthotic could be re-designed if necessary. The proposed automated procedure develops a pre-screening system to distinguish low arch and provide preventatives before it becomes symptomatic. Furthermore, non-symptom flatfoot can be detected at early stages and referred to medics for further diagnosis or treatment.

The effect of stretch-shortening magnitude and muscle-tendon unit length on performance enhancement in a stretch-shortening cycle.

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 < 0.05) increased in the SSC compared to the SHO contractions (in the range of 8.1-17.9%). No significant difference of joint torque was found in the steady-state for all SSC-magnitudes compared to the corresponding SHO contractions in session 1. In session 2, we found only significantly (p < 0.05) less depressed joint torque in the SSC at the longest muscle-tendon unit length compared to the corresponding SHO condition, without any differences in knee kinematics and fascicle behavior. Therefore, the physiological relevance of rFE might be particularly important for movements at greater muscle-tendon unit lengths.

Acute response on general and sport specific hip joint flexibility to training with novel sport device.

The study objective is to test general- and sport-specific adaption during a single training on the Flexibility Trainer. The device is designed to trigger residual muscle tone decreases of the hip-joint muscles by providing (nearly) isokinetic resistance during a full range of motion strength training in adduction/abduction and flexion/extension direction. Static hip flexion and abduction as well as kinematics of double side kicks were analysed on 15 participants before and after training (or rest for controls) to assess general flexibility and sport-specific movement range. Tests were recorded by a Vicon® motion capturing system. Static hip flexion and abduction as well as leg vector spreading angles (VSA) at different nodes of the kick were selected to determine adaptions of active and passive flexibility. Normalised hip joint moments, movement velocities and VSA were calculated to evaluate the training with the device. ANOVAs with 4-repeated measures and Friedman tests were performed to identify time differences and Bonferroni post-hoc test to identify between-subject effects. Significant differences were found for both static flexibility tests (Flexion = 13.65%; Abduction = 9.94%) and the VSA at specific action phases (≤15.15%). Results indicate that short-term adaptions when training with the Flexibility Trainer are exceeding comparable literature showing improved flexibility and sport-specific performance.