A Pipeline for Modelling of Ice-Hockey Stick Shape Deformation Using Actual Shot Video

In Ice-Hockey, performance of the player’s shots depends on their skill level, body strength as well as stick’s construction and stiffness. In fact, research suggests that one of the primary reasons that the elite players generate much faster shots is their ability to flex their hockey stick. Thus, reconstructing the deformable 3D shape of the stick during the course of a player shot has important applications in performance analysis of ice-hockey stick. We present a new, low cost, portable system to acquire videos of a player shot and to automatically reconstruct the stick shape’s deformation in 3D. This thesis is a sub-part and contributes towards the ultimate goal of the pipeline in many different ways. First, designing a mobile stereovision setup and its calibration, capturing a lot of data acquisitions with different players shooting in different styles. Second, developing a two step pruning methodology to prune structurally thin and fast moving ice-hockey stick from noisy reconstructed point cloud in 3D. Third, automating the process of initial rigid alignment of the stick template in the noisy reconstruction. Forth, reducing the effect of noise by using medial axis approximation approach and suppressing the hand occlusion effect on the final template bending by a curve fitting approach. This pipeline is also robust against different ice-hockey sticks along with different players, shooting at different styles

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment

Kinematic pattern of the drag-flick: a case study

The drag-flick is more efficient than hits or pushes when a penalty corner situation is in effect in field hockey. Previous research has studied the biomechanical pattern of the drag-flick, trying to find the cues for an optimal performance. On the other hand, some other studies have examined the most effective visual pick-up of relevant information in shots and goalkeeper anticipation. The aim of this study was to analyse the individual differences in the drag-flick pattern in order to provide relevant information for goalkeepers. One female skilled drag-flicker participated in the study. A VICON optoelectronic sy stem (Oxford Metrics, Oxford, UK) was used to capture the drag-flicks with six cameras. The results showed that the main significant differences between right and left shots (p<0.05) in the stick angles, stick minimum angular velocity and front foot-ball distance were when the front foot heel contacted the floor(T1) and at the minimum velocity of the stick, before the dragging action (T3). The findings showed that the most relevant information might be picked up at the ball-and-stick location before the dragging action

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis.

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes

Vision testing and visual training in sport

This thesis examines vision testing and visual training in sport. Through four related studies, the predictive ability of visual and perceptual tests was examined in a range of activities including driving and one-handed ball catching. The potential benefits of visual training methods were investigated (with particular emphasis on stroboscopic training), as well as the mechanisms that may underpin any changes. A key theme throughout the thesis was that of task representativeness; a concept by which it is believed the more a study design reflects the environment it is meant to predict, the more valid and reliable the results obtained are. Chapter one is a review of the literature highlighting the key areas which the thesis as a whole addresses. Chapter’s two to five include the studies undertaken in this thesis and follow the same format each time; an introduction to the relevant research, a methods section detailing the experimental procedure, a results section which statistically analysed the measures employed, and a discussion of the findings with reference to the existing literature. Finally, in chapter six the strengths and limitations of the thesis are considered, before suggestions are made for future studies, and concluding remarks made

Ears in motion: designing a toolkit for the sounds of sport

Athletes hear many different sounds while playing sport: the sounds of teammates, crowds, equipment, their own body, and their mind at work. Some hear nothing at all - a complete sonic blackout. This PhD outlines the design of a new &amp;ldquo;toolkit&amp;rdquo; for describing, recording, and representing this richly varied terrain. This toolkit has two components. The first is a notation system for describing the auditory experiences of athletes. The second is a wearable microphone system for capturing these sounds in new ways. The toolkit been used by the author and other athletes to create new works of sound design that represent the body in motion. In the design of this toolkit, I draw on a variety of disciplines that each touch on a particular aspect of sound in sport, including psychoacoustics, sports studies, anthropology, and media studies. While the auditory experience of athletes exists at the margins these disciplines, this PhD is an effort to draw these disparate fields together for a more comprehensive approach. The notation system, the first element in the toolkit, draws on these varied disciplines and defines new ways to identify specific sounds and their relationship to athletic performance. The majority of the design work in this PhD is devoted to creating new microphone systems for capturing the sounds of sport. While existing technologies tend to capture these sounds from the side-lines, these new microphones are worn on the athlete&amp;rsquo;s body or mounted to the athlete&amp;rsquo;s equipment. To enable recordings from the athlete&amp;rsquo;s body itself, these new microphones have been designed from the &amp;ldquo;ground up&amp;rdquo; &amp;ndash; from circuit design to PCB fabrication to software to industrial design to 3D fabrication. These microphones isolate specific sounds in the athlete&amp;rsquo;s environment to be re-assembled in the recording studio. This synthetic process of isolating and re-assembling sound allows listeners to examine these individual sounds in new levels of detail. For the sound designer, this presents new creative possibilities. For the athlete, this process can teach them to hear their sport in new ways. The toolkit is both diagnostic and creative. The research findings sit across three closely integrated advances: the toolkit comprising new notation and microphone design, insights into the auditory experience of athletes, and a framework for a transdisciplinary field in sport, media, and sound studies

Prédiction de la performance au hockey sur glace avec des évaluations de terrain

Au hockey sur glace, les évaluations physiques utilisées jusqu’à présent sont peu spécifiques à la tâche des joueurs et elles comprennent plusieurs limitations pour la prédiction de performance. La capacité d’accélération et de vitesse maximale en patinage sont grandement liées à la performance des joueurs et seulement certaines évaluations physiques hors glace ont montré des associations avec ces variables. Peu d’études ont considéré l’implication musculaire spécifique de la hanche qui est particulièrement sollicitée pendant le cycle de patinage. La science des données et l’utilisation d’outils d’apprentissage automatique ont connu un essor ces dernières années dans le milieu sportif. Elles ont montré de bonnes capacités prédictives et permettent de reconnaître des relations complexes et non linéaires entre les variables, pouvant surpasser les modèles statistiques standards et l’intuition humaine. Notre objectif principal est de prédire la performance en patinage chez les joueurs de hockey sur glace avec des évaluations physiques hors glace au moyen de modèles d’apprentissage automatique. Nous avons utilisé les données d’évaluations physiques de 72 athlètes effectuées lors des évaluations combinées de la Ligue de Hockey Junior Majeur du Québec. Nous avons utilisé l’accélération et la vitesse de patinage avant et arrière comme variables cibles. Les évaluations physiques standards effectuées lors de cette journée ainsi que la force isométrique maximale développée à l’articulation de la hanche pour ses six degrés-de-liberté ont été les variables d’entrées pour un modèle linéaire (LassoCV) et un modèle non linéaire (xgboost). Nos modèles d’apprentissage automatique montrent de bonnes prédictions pour les variables de vitesse avant en montrant une erreur absolue moyenne en pourcentage (MAPE) inférieur à 2,96±2,15%, tandis que les erreurs de prédiction pour les variables d’accélération et de vitesse arrière demeurent élevées (MAPE : > 4,46±4,15%). Le saut en longueur et la force isométrique en flexion de la hanche ont montré le plus d’importance pour l’accélération sur 5 mètres (5F et 5B) et la vitesse sur 30 mètres en patinage dans les deux directions (30F et 30B). Suite à une validation des modèles, les entraîneurs pourraient donc utiliser les résultats obtenus aux évaluations physiques pour développer la performance en vitesse maximale avant des joueurs de hockey sur glace.In ice hockey, the physical assessments used to date are not very specific to the task of ice hockey players and may have several limitations for predicting performance. Acceleration ability and maximum speed are highly related to player performance and only some off-ice physical assessments showed associations with these variables. Few studies have considered involvement of specific hip muscles, which are particularly solicited during the skating cycle. The data science and the use of machine learning tools have grown in recent years in the sports community. It has shown to improve prediction and has the ability to recognize complex and non-linear relationships between variables, and thus, can outperform standard statistical models and human intuition. Our main objective is the prediction of skating performance of ice hockey players with physical assessments and the use of machine learning models. We used data from physical assessment of 72 athletes who attended at the combine of the Quebec Major Junior Hockey League. We used acceleration and speed in forward and backward direction as target variables. The standard physical assessment performed on that day and isometric hip joint strength developed on its six degrees of freedom were input variables for a linear (LassoCV) and a non-linear (xgboost) model. Our machine learning models show good prediction errors by showing a mean absolute percentage error (MAPE) of less than 2.96±2.15% for the forward speed variables, while the prediction errors for acceleration and backward speed variables remain high (MAPE: > 4.46±4.15%). The broad jump and the isometric strength developed in hip flexion showed the most importance for acceleration over 5 meters (5F and 5B) and sprint over 30 meters (30F and 30B) in both skating directions. After validation of the models, coaches could use the results of these physical assessments to develop maximum forward speed performance of ice hockey players