MODELLING AND PROGNOSIS OF COMPETITIVE PERFORMANCES IN ELITE SWIMMING

The study demonstrates that the performance of an elite female swimmer in the finals of the 200 m backstroke at the Olympic Games 2000 in Sydney can be predicted by means of the nonlinear mathematical method of a neural back-propagation network. The analysis included the performance output data of 19 competitions prior to the Olympics within a time period of 95 successive weeks and the training input data of the last four weeks prior to each competition. The training data were divided into two phases: (1) a two-week taper cycle, and (2) an earlier two-week high load cycle. The trained neural network was not only able to model the 19 competitive performances, but also to predict the performance in the semi final of the Olympic Games in Sydney on the basis of the two sets of training data during the preparation before that specific competition

A NONLINEAR APPROACH TO THE ANALYSIS AND MODELING OF TRAINING AND ADAPTATION IN SWIMMING

The purpose of the study was to demonstrate that the adaptative behavior of an elite female swimmer (Olympic silver medalist in the 400 m freestyle) can be modeled by means of the nonlinear mathematical method of a neural backpropagation network. Therefore, the training process of 107 successive weeks was carefully controlled and documented. For the data analysis a multilayer perceptron network was trained with the performance output data of 28 competitions within that time period and the training input data of the last four weeks prior to the respective competitions. After the iterative training procedure the neural network is able to model the resulting competitive performances on the basis of the training data from the two-week-taper phase and also from the earlier two-week-overload phase preceeding the respective competitions with high precision

Towards Machine Learning on data from Professional Cyclists

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data (big data) is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.Comment: Accepted for the 12th World Congress on Performance Analysis of Sports, Opatija, Croatia, 201

Modelling the relationship between relative load and match outcome in junior tennis players

The acute:chronic workload ratio (ACWR) is a metric that can be used to monitor training loads during sport. Over the last decade researchers have investigated how this metric relates to injury, yet little consideration has been given to how this metric interacts with performance. Two prospective longitudinal studies were implemented investigating internal and external ACWRs and match outcome in junior tennis players. Forty-two and 24 players were recruited to participate in the internal and external load studies, respectively. Internal load was measured using session rate of perceive exertion, while external load was defined as total swing counts. The main dependent variable was tennis match performance which was extracted from the universal tennis rating website. The ACWR for internal and external load were the primary independent variables. Acute load was defined as the total load for one week, while a 4-week rolling average represented chronic load. There were no significant associations between internal (p-value = .23) or external (p-value = .81) ACWR and tennis match performance as assessed by multivariate regressions. The ACWRs in these datasets were close to 1.00, thus a balanced training load was undertaken by these athletes upon entering match play but was not related to match success

Modeling the relationship between physical training and performance in endurance sports

Exercise regimen is an integral part of sports coaching, the goal of which is to improve an individual's athletic performance. In an effort to better understand and optimize athletic performance, exercise physiologists have developed mathematical models of physical training and performance. Banister model and "Training Stress Score", a method to quantify training load have been presented in this thesis. An overview of existing literature has been conducted and Banister model has been tested on training data of six road cyclists. Model's prediction quality has been proven poor and its shortcomings have been exposed during evaluation

Implementation of Oxymetry Sensors for Cardiovascular Load Monitoring When Physical Exercise

The performance condition of an athlete must always be maintained, one way to maintain that performance is by training. Each individual has different abilities and physiological responses in receiving the portion of the exercise. Physical exercise that exceeds the body's ability can worsen the condition of the athlete itself which can result in excessive fatigue (overtraining) or can even result in injury. Therefore a system is needed to monitor the condition of the physiological response when given the intensity of the training load so that the portion of the training provided provides positive benefits for the athlete. This system was developed using an oxymetry sensor, microcontroller and wifi module ESP8266.  This system is used to collect heart rate and oxygen saturation data, then with the existing formula the heart rate value is converted to a CVL (Cardiovascular Load) value to determine the level of fatigue in athletes when given the intensity of the training load. By using a web-based application, measurement data is displayed in realtime to make it easier to see the results of monitoring. From the experimental results the system can monitor changes in the physiological condition of the athlete when given the intensity of the training load. Finally, the developed system can collect athlete's physiological data, and can store the data in a database and display it in a web application

Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation