Classification of table tennis strokes using a wearable device and deep learning

The analysis of sports using everyday mobile devices is an area that has been increasingly explored aiming to help the user to improve in all aspects of the sport. The objective of the work proposed for this dissertation is to developed application capable of detecting strokes in table tennis using the iPhone and the Apple Watch, in which a recorded table tennis strokes data set performed by several table tennis athletes was created to help develop the application. Since the Artificial Intelegence area is increasingly present in our daily lives, the motivation in this work is to have a first contact with the current state of AI, the technologies available and most used in today’s present, and as within the company, it was intended to begin research in this area, mainly using Apple devices, it was decided to try and create a mobile application capable of detecting strokes performed in table tennis that would work with devices capable of AI processing, in order to provide statistical data to help table tennis athletes and coaches, which can later be sell for. After a study of devices available on the apple market with the necessary capabilities for the purpose of the work, it was concluded that for this work, the devices to be used would be the iPhone (above the X model) and the Apple Watch (above the model 5). Also because there were no public table tennis data set available, a methodology was developed with the objective of capturing table tennis strokes trough motion data. The recording of motion data was done by using an application capable of recording sensors data using the apple watch who was used by each athlete on the wrist. The sensors used to record motion data were accelerometer and gyroscope, and the capture methodology was planned and overseen by coaches and athletes. From the methodology created, 2 base data sets were created. One consisting of a short interval between strokes and the second and last with a bigger interval between strokes. From these 2 data sets, 3 more were created with different pre processing configurations applied followed by a filtering and reformatting of data to the necessary format for the creation of a Deep Learning model. To generate a DL classifier model, two approaches were tested, one by using Create ML, and the other by using Convolution Neural Network-Long Short Term Memory and Convolution Neural Network-Long Short Term Memory architecture. To evaluate the models, statistics generated from training were saved during model testing and creation. Create ML data set classifier models showed average performance except in one data set, with the generated classifier model having a maximum performance of 89.66% F1 score while CNN-LSTM and ConvLSTM approach generated good performance from all data set generated classifier models with the best classifier being the ConvLSTM with a 97.33% F1 score. After the creation of this same model, development of the application was performed consisting of two parts, one on the iPhone where it is possible to see the statistics and another on the Apple Watch where the ML model is executed and the stroke performed is detected being then sent to the application on the iPhone. The final step consisted on evaluation of the application during a live game scenario followed by an user rating application feedback questionnaire on athletes and coaches. Final application feedback was positive across all subjects with recommendations to the application interface and improvements to the classifier model. The live game application scenario with the generated classifier model obtained a 80% correct labelled strokes

Large-Scale Method for Identifying the Relationships between Racket Properties and Playing Characteristics

The application of advanced engineering in tennis has seen vast changes in playing styles, racket materials and racket design. Although previous researchers have investigated the effects of racket properties during and post ball-racket impacts, the studies focused on limited variation within racket properties. As regulators of the game, the International Tennis Federation monitor racket performance, however, standard laboratory test methods do not exist. The establishment of appropriate testing standards would further the understanding of the effect of racket properties, or racket property combinations, whilst reducing discrepancies between studies. This work aims to identify racket properties resulting in distinct behavioural characteristics through the development of a test protocol accurately simulating different forehand conditions found within the field of play. Classification of the raw player testing data, previously collected from the 2006 Wimbledon Qualifying tournament, identified the characteristics of three specific forehand shots used within the field of play. The forehand shots were identified as either topspin or slice, each possessing different defining characteristics. The results from the player shot classification, five impact positions varying along the longitudinal and transverse axis, and a restrictive torque value representative of hand grip were used in the development of a laboratory test protocol capable of realistically and accurately simulating different forehand shots. Using the developed test protocol for a typical topspin and slice forehand, a total of 39 rackets of varying properties and property combinations, were repeatedly impacted at the relative impact positions. A three-dimensional analysis, through the use of two Phantom High-Speed video cameras, recorded the experimental outputs within a fully calibrated control volume. Reducing the complexity of the data, the experimental outputs were interpreted using clustering techniques, identifying clusters of rackets sharing similar behavioural characteristics. A total of four clusters of distinct behavioural characteristics were identified for both the topspin and slice forehand. Analysis of these clusters revealed that rackets of diverse property combinations can produce similar behavioural characteristics, indicating the importance of varying racket property combinations in this area of research. The relationships between the behavioural clusters and subsequent racket properties were identified using multinomial logistic regression. (MNLR). Investigations revealed a complex dynamic relationship between racket properties and racket behaviour, such that racket behaviour, or performance, is dependent on its physical properties as both individual and interacting entities and are specific to shot type. Therefore, to gain a complete understanding regarding the effects of racket properties on the nature of the game, investigations consider the combined effects of racket properties and their relationship(s) to specific shot types found within the field of play

Biomechanical Spectrum of Human Sport Performance

Writing or managing a scientific book, as it is known today, depends on a series of major activities, such as regrouping researchers, reviewing chapters, informing and exchanging with contributors, and at the very least, motivating them to achieve the objective of publication. The idea of this book arose from many years of work in biomechanics, health disease, and rehabilitation. Through exchanges with authors from several countries, we learned much from each other, and we decided with the publisher to transfer this knowledge to readers interested in the current understanding of the impact of biomechanics in the analysis of movement and its optimization. The main objective is to provide some interesting articles that show the scope of biomechanical analysis and technologies in human behavior tasks. Engineers, researchers, and students from biomedical engineering and health sciences, as well as industrial professionals, can benefit from this compendium of knowledge about biomechanics applied to the human body

Statistically modelling tennis racket impacts with six degrees of freedom

The International Tennis Federation (ITF) is responsible for protecting the nature of tennis. The ITF uses computational models to predict how trends in equipment parameters could affect the games future. The current ball-racket impact model is limited to non-spinning, on-axis, normal ball impact simulations. The aim of this project was to develop a model of oblique, spinning, on- and off-axis ball-racket impacts. Large scale test data (n > 1000) was collected using an impact rig and calibrated high-speed cameras. Impacts for a range of realistic velocities, spin rates and impact locations were collected, measured using automated image processing algorithms to digitise ball centroids. An established spin measurement method was improved to correct for perspective errors associated with the proximity of the cameras to the test volume. The automated algorithms were validated with experimental data and manual methods. Multi-variate polynomial models to predict the lateral and vertical components of rebound velocities and rebound spin rate were trained and validated using a curve fitting toolbox and ‘n-fold and leave one out cross-validation’ method. Second order models best fit the training data, with the low predictive errors. Root-mean-squared errors were calculated using a test dataset, independent of the training data. These were 0.57 m·s-1 for the lateral rebound velocity model, 0.48 m·s-1 for the vertical rebound velocity model and 30.5 rad·s-1 for the rebound spin rate model. Variance was partially explained by experimentally established inherent variability of the ball and stringbed. Model output confidence was established by simulating small changes in model inputs. The simulated lateral and vertical components of rebound velocity, but not the simulated spin rate, were an order of magnitude greater than the measurement precision. The new models were combined with ball aerodynamics and ball-to-surface impact models to simulate tennis court trajectories for oblique, spinning, on- and off-axis ball-racket impacts. Increasing stringbed stiffness or the lateral offset of impact location were found to decrease rebound velocity and increase rebound angle – markedly so for a 60 mm lateral offset. Increasing lateral offset also increased the rebound spin rate

Analysis of the backpack loading efects on the human gait

Gait is a simple activity of daily life and one of the main abilities of the human being. Often during leisure, labour and sports activities, loads are carried over (e.g. backpack) during gait. These circumstantial loads can generate instability and increase biomechanicalstress over the human tissues and systems, especially on the locomotor, balance and postural regulation systems. According to Wearing (2006), subjects that carry a transitory or intermittent load will be able to find relatively efficient solutions to compensate its effects.info:eu-repo/semantics/publishedVersio

Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

Sports Materials

To further improve the level of correlation between these finite element models and lab-simulated bat/ball impacts, the material behavior for these wood species must also be characterized at strain rates comparable to those experienced ..

Assessment of Physical Fitness and Training Effect in Individual Sports

Physical fitness is the basis for the success of players in sports, and its monitoring makes it possible to assess the effectiveness of training and identify possible errors. During training, thanks to the use of control results, these activities are modified, which better prepares players for competition. This Special Issue, entitled "Assessment of Physical Fitness and the Effect of Training in Individual Sports" presents the results of coaching control and the results of monitoring progression in training, as well as an assessment of the physical fitness of athletes practicing individual sports