Jumping towards field-based ground reaction force estimation and assessment with OpenCap.

Low-cost and field-viable methods that can simultaneously assess external kinetics and kinematics are necessary to enhance field-based biomechanical monitoring. The aim of this study was to determine the accuracy and usability of ground reaction force (GRF) profiles estimated from segmental kinematics, measured with OpenCap (a low-cost markerless motion-capture system), during common jumping movements. Full-body segmental kinematics were recorded for fifteen recreational athletes performing countermovement, squat, bilateral drop, and unilateral drop jumps, and used to estimate vertical GRFs with a mechanics-based method. Eleven distinct performance-, fatigue-, or injury-related GRF variables were then validated against a gold-standard force platform. Across jumping movements, a total of six and three GRF variables were estimated with a bias or limits of agreement <5 % respectively. Bias and limits of agreement were between 5 and 15 % for seventeen and nineteen variables respectively. Moreover, we show that estimated force variables with a bias <15 % can adequately assess the within-athlete changes in GRF variables between jumping conditions (arm swing or leg dominance). These findings indicate that using a low-cost and field-viable markerless motion capture system (OpenCap) to estimate and assess GRF profiles during common jumping movements is approaching acceptable limits of accuracy. The presented method can be used to monitor force variables of interest and examine underlying segmental kinematics. This application is a jump towards researchers and sports practitioners performing biomechanical monitoring of jumping efficiently, regularly, and extensively in field settings.

The effect of non-verbal mimicry on evaluations in interactions with cognitively (dis)similar individuals.

Non-verbal mimicry (i.e., being posturally similar by copying another person's body language) has been shown to increase evaluations of the mimicker. Concurrently, extensive research in social psychology has demonstrated a negative effect on interpersonal evaluations when one perceives others as cognitively dissimilar, often resulting in interpersonal conflicts. Across two experiments (Experiment 1: N = 159, Experiment 2: N = 144), we tested our hypotheses that mimicry, compared with no mimicry, will make mimickers come across as more likable and competent regardless of whether they were perceived as cognitively dissimilar or not (Experiment 1) and regardless of the extent to which they were perceived as cognitively dissimilar (Experiment 2). Broadly, we found support for our hypotheses, and via mediation sensitivity analyses, we found that the effect of mimicry, at least for likability, was mediated by participants' perceived personal similarity to the mimicker. Non-verbal mimicry may thus be one way of alleviating interpersonal conflicts via increasing perceptions of personal similarity regardless of initial cognitive dissimilarity.

Reliability and sensitivity to change of post-match physical performance measures in elite youth soccer players.

Introduction: To effectively monitor post-match changes in physical performance, valid, reliable and practical measures which are sensitive to change are required. This study aimed to quantify test-retest reliability and sensitivity to change of a range of physical performance measures recorded during an isometric posterior chain (IPC) lower-limb muscle test and a countermovement jump (CMJ) test. Methods: Eighteen Italian Serie A academy soccer players performed three IPC repetitions per limb and five CMJ trials in 4 testing sessions. Test-retest reliability was evaluated between two testing sessions seven days apart using typical error of measurement, coefficient of variation and intraclass correlation coefficient. Sensitivity to change was assessed on two additional testing sessions performed before and immediately after a soccer match through Hedges' g effect size (g) and comparisons to typical error. Results: Absolute reliability (coefficient of variations) ranged from 1.5 to 8.8%. IPC and CMJ measures demonstrated moderate to excellent relative reliability (intraclass correlation coefficients ranged from 0.70 to 0.98). A wide range of physical performance measures showed significant alterations post-match (p < 0.05; g: small to moderate). IPC peak force and torque, CMJ reactive strength index modified, CMJ eccentric forces (mean breaking force, mean deceleration force, peak force, force at zero velocity) and CMJ mean power measures had post-match changes greater than their typical variation, demonstrating acceptable sensitivity in detecting performance changes at post-match. Discussion: IPC peak force and torque, CMJ reactive strength index modified, CMJ eccentric phase forces and CMJ mean power were found to be both reliable and sensitive to change, and thus may be appropriate for monitoring post-match neuromuscular performance in youth soccer population.

The non-sagittal knee moment vector identifies 'at risk' individuals that the knee abduction moment alone does not.

Multi-planar forces and moments are known to injure the anterior cruciate ligament (ACL). In ACL injury risk studies, however, the uni-planar frontal plane external knee abduction moment is frequently studied in isolation. This study aimed to determine if the frontal plane knee moment (KM-Y) could classify all individuals crossing a risk threshold compared to those classified by a multi-planar non-sagittal knee moment vector (KM-YZ). Recreationally active females completed three sports tasks-drop vertical jumps, single-leg drop vertical jumps and planned sidesteps. Peak knee abduction moments and peak non-sagittal resultant knee moments were obtained for each task, and a risk threshold of the sample mean plus 1.6 standard deviations was used for classification. A sensitivity analysis of the threshold from 1-2 standard deviations was also conducted. KM-Y did not identify all participants who crossed the risk threshold as the non-sagittal moment identified unique individuals. This result was consistent across tasks and threshold sensitivities. Analysing the peak uni-planar knee abduction moment alone is therefore likely overly reductionist, as this study demonstrates that a KM-YZ threshold identifies 'at risk' individuals that a KM-Y threshold does not. Multi-planar moment metrics such as KM-YZ may help facilitate the development of screening protocols across multiple tasks.

Watching the mimickers: Mimicry and identity in observed interactions.

Mimicry enhances one's judgments of the mimicker when it is directed toward the self. However, often interactions do not involve only the participants; observers also judge people, and such judgments are influenced by social identities. So, does mimicry also have positive effects even on observers' evaluations of the mimicker? Furthermore, does that hold even if the mimicker is an out-group member? To answer these questions, we used two video experiments (N1 = 377; N2 = 670) to compare mimicry and neutral (no mimicry) interactions between two individuals who were primed to be in either the participant's in-group or out-group. In both studies, we found the expected negative out-group bias when participants observed the neutral interaction but only for competence-related variables. However, such biases were diminished in the mimicry condition, indicating that mimicry, even when it is merely observed and directed at someone else, may alter mimicker-related attitudes stemming from social identities. Our findings therefore contribute to the literature on reducing intergroup prejudice by demonstrating the behavior-based malleability of a negative out-group bias. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Change of direction frequency off the ball: new perspectives in elite youth soccer.

PURPOSE: The aim of this study was to investigate the frequency of change of directions (COD) and examine the influences of position, leg dominance and anthropometrics on COD in elite youth soccer match play. METHODS: Twenty-four elite male English Premier League (EPL) academy players (19.0 ± 1.9 years) were individually recorded during ten competitive U18s and U23s matches. Video footage of individual players was analysed using a manual notation system to record COD frequency, direction, estimated angle and recovery time. The influences of position, anthropometrics and leg dominance were accounted for. RESULTS: Elite youth soccer players performed on average 305 ± 50 CODs with on average 19.2 ± 3.9 seconds of recovery. The frequency of CODs was independent of position, leg dominance, anthropometry and occurred equally between left and right direction and forwards and backwards direction. CODs were mostly ≤90° (77%) and there were significantly less CODs in the 2nd half (-29, ES = 1.23, P< 0.001). The average and peak within match demands within 15 and 5-minute periods were 49 and 62 CODs, and 16 and 25 CODs, respectively. CONCLUSION: This study is the first to illustrate COD frequencies of elite youth soccer match play, providing practitioners guidance to prepare soccer players for competitive match demands.

Glimpsing the Impossible: How Artificially Enhanced Targets Improve Elite Performance.

In 2009, elite swimming introduced polyurethane "supersuits," which artificially enhanced performances and facilitated 43 world records at the World Championships, before being prohibited from 2010. This transient, artificial improvement spike created a natural experiment to examine the effect of "impossible" targets on subsequent performances. Analyses revealed that swimming speeds at global championships in the postsupersuit period (2011-2017) were substantially faster than predicted from the presupersuit period (2000-2007). These results suggest that the transient, artificially enhanced performances of the supersuit era recalibrated targets upward-acting as goals-and improved subsequent performances beyond previous trajectories (d = 0.64; 0.70%). Contributing to psychological goal-setting theory, the positive relationship between the size of the transient, artificial improvement (i.e., goal difficulty) and subsequent performance was curvilinear, increasing at a decreasing rate before improvements plateaued. Overall, the research demonstrates the potential for elite athletes to exceed perceived human limits after expectations have been recalibrated upward.

Simultaneously assessing amplitude and temporal effects in biomechanical trajectories using nonlinear registration and statistical nonparametric mapping.

Biomechanical trajectories generally embody amplitude and temporal effects, but these effects are often analyzed separately. Here we demonstrate how amplitude-phase separation techniques from the statistics literature can be used to simultaneously analyze both. The approach hinges on nonlinear registration, which temporally warps trajectories to minimize timing effects, and the resulting optimal time warps can be combined with the resulting amplitudes in a simultaneous test. We first analyzed two simulated datasets with controlled amplitude and temporal effects to demonstrate how amplitude-timing separation can avoid incorrect conclusions from common amplitude-only hypothesis testing. We then analyzed two experimental datasets, demonstrating how amplitude-phase separation can yield unique perspectives on the relative contributions of amplitude and timing effects embodied in biomechanical trajectories. Last, we show that the proposed approach can be sensitive to procedural and parameter specifics, so we recommend that these sensitivities should be explored and reported.

An ecological dynamics approach to ACL injury risk research: a current opinion.

Research of non-contact anterior cruciate ligament (ACL) inj1ury risk aims to identify modifiable risk factors that are linked to the mechanisms of injury. Information from these studies is then used in the development of injury prevention programmes. However, ACL injury risk research often leans towards methods with three limitations: 1) a poor preservation of the athlete-environment relationship that limits the generalisability of results, 2) the use of a strictly biomechanical approach to injury causation that is incomplete for the description of injury mechanisms, 3) and a reductionist analysis that neglects profound information regarding human movement. This current opinion proposes three principles from an ecological dynamics perspective that address these limitations. First, it is argued that, to improve the generalisability of findings, research requires a well-preserved athlete-environment relationship. Second, the merit of including behaviour and the playing situation in the model of injury causation is presented. Third, this paper advocates that research benefits from conducting non-reductionist analysis (i.e., more holistic) that provides profound information regarding human movement. Together, these principles facilitate an ecological dynamics approach to injury risk research that helps to expand our understanding of injury mechanisms and thus contributes to the development of preventative measures.

Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units.

Changes of direction (COD) are an important aspect of soccer match play. Understanding the physiological and biomechanical demands on players in games allows sports scientists to effectively train and rehabilitate soccer players. COD are conventionally recorded using manually annotated time-motion video analysis which is highly time consuming, so more time-efficient approaches are required. The aim was to develop an automated classification model based on multi-sensor player tracking device data to detect COD > 45°. Video analysis data and individual multi-sensor player tracking data (GPS, accelerometer, gyroscopic) for 23 academy-level soccer players were used. A novel 'GPS-COD Angle' variable was developed and used in model training; along with 24 GPS-derived, gyroscope and accelerometer variables. Video annotation was the ground truth indicator of occurrence of COD > 45°. The random forest classifier using the full set of features demonstrated the highest accuracy (AUROC = 0.957, 95% CI = 0.956-0.958, Sensitivity = 0.941, Specificity = 0.772. To balance sensitivity and specificity, model parameters were optimised resulting in a value of 0.889 for both metrics. Similarly high levels of accuracy were observed for random forest models trained using a reduced set of features, accelerometer-derived variables only, and gyroscope-derived variables only. These results point to the potential effectiveness of the novel methodology implemented in automatically identifying COD in soccer players.

Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis.

Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised 1D effects. This study exemplifies 1D sample size estimation using typical biomechanical signals and contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 and 2019 were reviewed to characterise current practice. Sample size estimation occurred in approximately 4% of 653 papers and reporting practice was mixed. To estimate sample sizes, common biomechanical signals were sourced from the literature and 1D effects were generated artificially using the open-source power1d software. Smooth Gaussian noise was added to the modelled 1D effect to numerically estimate the sample size required. Sample sizes estimated using 1D power procedures varied according to the characteristics of the dataset, requiring only small-to-moderate sample sizes of approximately 5-40 to achieve target powers of 0.8 for reported 1D effects, but were always larger than 0D sample sizes (from N + 1 to >N + 20). The importance of a priori sample size estimation is highlighted and recommendations are provided to improve the consistency of reporting. This study should enable researchers to construct 1D biomechanical effects to address adequately powered, hypothesis-driven, predictive research questions.

The inter-laboratory equivalence for lower limb kinematics and kinetics during unplanned sidestepping.

Much inter-intra-tester kinematic and kinetic repeatability research exists, with a paucity investigating inter-laboratory equivalence. The objective of this research was to evaluate the inter-laboratory equivalence between time varying unplanned kinematics and moments of unplanned sidestepping (UnSS). Eight elite female athletes completed an established UnSS procedure motion capture laboratories in the UK and Australia. Three dimensional time varying unplanned sidestepping joint kinematics and moments were compared. Discrete variables were change of direction angles and velocity. Waveform data were compared using mean differences, 1D 95%CI and RMSE. Discrete variables were compared using 0D 95% CI. The mean differences and 95%CI for UnSS kinematics broadly supported equivalence between laboratories (RMSE≤5.1°). Excluding hip flexion/extension moments (RMSE = 1.04 Nm/kg), equivalence was also supported for time varying joint moments between laboratories (RMSE≤0.40 Nm/kg). Dependent variables typically used to characterise UnSS were also equivalent. When consistent experimental and modelling procedures are employed, consistent time varying UnSS lower limb joint kinematic and moment estimates between laboratories can be obtained. We therefore interpret these results as a support of equivalence, yet highlight the challenges of establishing between-laboratory experiments or data sharing, as well as establishing appropriate ranges of acceptable uncertainty. These findings are important for data sharing and multi-centre trials.

PCA of waveforms and functional PCA: A primer for biomechanics.

Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework. This technical note demonstrates that PCA of waveforms is the most rudimentary form of FDA, and consequently can be rationalised within the FDA framework of statistical processes. Mathematical proofing applied demonstrations of both techniques, and an example of when fPCA may be of greater benefit to control over smoothing of functional principal components is provided using an open access motion sickness dataset. Finally, open access software is provided with this paper as means of priming the biomechanics community for using these methods as a part of future functional data explorations.

Biomechanical loading during running: can a two mass-spring-damper model be used to evaluate ground reaction forces for high-intensity tasks?

Running impact forces expose the body to biomechanical loads leading to beneficial adaptations, but also risk of injury. High-intensity running tasks, especially, are deemed highly demanding for the musculoskeletal system, but loads experienced during these actions are not well understood. To eventually predict GRF and understand the biomechanical loads experienced during such activities in greater detail, this study aimed to (1) examine the feasibility of using a simple two mass-spring-damper model, based on eight model parameters, to reproduce ground reaction forces (GRFs) for high-intensity running tasks and (2) verify whether the required model parameters were physically meaningful. This model was used to reproduce GRFs for rapid accelerations and decelerations, constant speed running and maximal sprints. GRF profiles and impulses could be reproduced with low to very low errors across tasks, but subtler loading characteristics (impact peaks, loading rate) were modelled less accurately. Moreover, required model parameters varied strongly between trials and had minimal physical meaning. These results show that although a two mass-spring-damper model can be used to reproduce overall GRFs for high-intensity running tasks, the application of this simple model for predicting GRFs in the field and/or understanding the biomechanical demands of training in greater detail is likely limited.

Prescribing joint co-ordinates during model preparation in OpenSim improves lower limb unplanned sidestepping kinematics.

OBJECTIVES: Investigate how prescribing participant-specific joint co-ordinates during model preparation influences the measurement agreement of inverse kinematic (IK) derived unplanned sidestepping (UnSS) lower limb kinematics in OpenSim in comparison to an established direct kinematic (DK) model. DESIGN: Parallel forms repeatability. METHODS: The lower limb UnSS kinematics of 20 elite female athletes were calculated using: 1) an established DK model (criterion) and, 2) two IK models; one with (IKPC) and one without (IK0) participant-specific joint co-ordinates prescribed during the marker registration phase of model preparation in OpenSim. Time-varying kinematic analyses were performed using one dimensional (1D) statistical parametric mapping (α=0.05), where zero dimensional (0D) Root Mean Squared Error (RMSE) estimates were calculated and used as a surrogate effect size estimates. RESULTS: Statistical differences were observed between the IKPC and DK derived kinematics as well as the IK0 and DK derived kinematics. For the IKPC and DK models, mean kinematic differences over stance for the three dimensional (3D) hip joint, 3D knee joint and ankle flexion/extension (F/E) degrees of freedom (DoF) were 46±40% (RMSE=5±5°), 56±31% (RMSE=7±4°) and 3% (RMSE=2°) respectively. For the IK0 and DK models, mean kinematics differences over stance for the 3D hip joint, 3D knee joint and ankle F/E DoF were 70±53% (RMSE=14±11°), 46±48% (RMSE=8±7°) and 100% (RMSE=11°) respectively. CONCLUSIONS: Prescribing participant-specific joint co-ordinates during model preparation improves the agreement of IK derived lower limb UnSS kinematics in OpenSim with an established DK model, as well as previously published in-vivo knee kinematic estimates.

Evacuation behaviors and emergency communications: An analysis of real-world incident videos.

Emergencies such as fires and terrorist attacks pose risks of injuries and fatalities, which can be exacerbated by delayed, ill-informed, or unmanaged responses. Effective emergency communication strategies could be used to better inform people and reduce these risks. This research analyzes videos of real-world emergencies to: (a) identify people's observed behaviors that increase risk during evacuations, and (b) examine which emergency communication strategies might reduce risk behaviors. We analyzed 126 publicly available videos of emergency evacuations in different emergencies (e.g., fire, terror attack, evacuation alarm, perceived threat). We found evidence of three types of risk behaviors (delayed response, filming, running) and four emergency communication strategies (evacuation alarm, staff guiding people to exits, general prerecorded message, live announcement). Our analyses suggest that having staff guide people to exits is the most effective strategy for promoting faster and more effective responses. However, neither live announcements nor pre-recorded messages were associated with delayed responses, while evacuation alarms were associated with more delayed responses than other communication strategies. Although people filming the incident was unrelated to staff interactions, it occurred more with alarms sounding and prerecorded messages, suggesting that these emergency communications might not prevent filming. Compared to no communications, all emergency communication strategies reduced running during evacuations. We discuss the implications of this research for identifying effective emergency communication strategies and reducing risk-increasing evacuation behaviors.

Multidimensional Ground Reaction Forces and Moments From Wearable Sensor Accelerations via Deep Learning.

OBJECTIVE: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field. METHODS: Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. RESULTS: The proposed deep learning workbench achieved correlations to ground truth, by maximum discrete GRF component, of vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87 (sidestepping), with the strongest mean recorded across GRF components 0.89, and for GRM 0.65 (both sidestepping). CONCLUSION: These best-case correlations indicate the plausibility of the approach although the range of results was disappointing. The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study. SIGNIFICANCE: Coaching, medical, and allied health staff could ultimately use this technology to monitor a range of joint loading indicators during game play, with the aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

Whole-body dynamic stability in side cutting: Implications for markers of lower limb injury risk and change of direction performance.

Control of the centre of mass (CoM) whilst minimising the use of unnecessary movements is imperative for successful performance of dynamic sports tasks, and may indicate the condition of whole-body dynamic stability. The aims of this study were to express movement strategies that represent whole-body dynamic stability, and to explore their association with potentially injurious joint mechanics and side cutting performance. Twenty recreational soccer players completed 45° unanticipated side cutting. Five distinct whole-body dynamic stability movement strategies were identified, based on factors that influence the medial ground reaction force (GRF) vector during ground contact in the side cutting manoeuvre. Using Statistical Parametric Mapping, the movement strategies were linearly regressed against selected performance outcomes and peak knee abduction moment (peak KAM). Significant relationships were found between each movement strategy and at least one selected performance outcome or peak KAM. Our results suggest excessive medial GRFs were generated through sagittal plane movement strategies, and despite being beneficial for performance aspects, poor sagittal plane efficiency may destabilise control of the CoM. Frontal plane hip acceleration is the key non-sagittal plane movement strategy used in a corrective capacity to moderate excessive medial forces. However, whilst this movement strategy offered a way to retrieve control of the CoM, mitigating reduced whole-body dynamic stability, it also coincided with increased peak KAM. Overall, whole-body dynamic stability movement strategies helped explain the delicate interplay between the mechanics of changing direction and undesirable joint moments, providing insights that might support development of future intervention strategies.

A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities.

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r2 coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r2 was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.

Ergonomists as designers: computational modelling and simulation of complex socio-technical systems.

Contemporary ergonomics problems are increasing in scale, ambition, and complexity. Understanding and creating solutions for these multi-faceted, dynamic, and systemic problems challenges traditional methods. Computational modelling approaches can help address this methodological shortfall. We illustrate this potential by describing applications of computational modelling to: (1) teamworking within a multi-team engineering environment; (2) crowd behaviour in different transport terminals; and (3) performance of engineering supply chains. Our examples highlight the benefits and challenges for multi-disciplinary approaches to computational modelling, demonstrating the need for socio-technical design principles. Our experience highlights opportunities for ergonomists as designers and users of computational models, and the instrumental role that ergonomics can play in developing and enhancing complex socio-technical systems. Recognising the challenges inherent in designing computational models, we reflect on practical issues and lessons learned so that computational modelling and simulation can become a standard and valuable technique in the ergonomists' toolkit. Practitioner summary: This paper argues that computational modelling and simulation is currently underutilised in ergonomics research and practice. Through example applications illustrating the benefits, limitations, and opportunities of such approaches, this paper is a point of reference for researchers and practitioners using computational modelling to explore complex socio-technical systems.