The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

Vision Based Activity Recognition Using Machine Learning and Deep Learning Architecture

Human Activity recognition, with wide application in fields like video surveillance, sports, human interaction, elderly care has shown great influence in upbringing the standard of life of people. With the constant development of new architecture, models, and an increase in the computational capability of the system, the adoption of machine learning and deep learning for activity recognition has shown great improvement with high performance in recent years. My research goal in this thesis is to design and compare machine learning and deep learning models for activity recognition through videos collected from different media in the field of sports. Human activity recognition (HAR) mostly is to recognize the action performed by a human through the data collected from different sources automatically. Based on the literature review, most data collected for analysis is based on time series data collected through different sensors and video-based data collected through the camera. So firstly, our research analyzes and compare different machine learning and deep learning architecture with sensor-based data collected from an accelerometer of a smartphone place at different position of the human body. Without any hand-crafted feature extraction methods, we found that deep learning architecture outperforms most of the machine learning architecture and the use of multiple sensors has higher accuracy than a dataset collected from a single sensor. Secondly, as collecting data from sensors in real-time is not feasible in all the fields such as sports, we study the activity recognition by using the video dataset. For this, we used two state-of-the-art deep learning architectures previously trained on the big, annotated dataset using transfer learning methods for activity recognition in three different sports-related publicly available datasets. Extending the study to the different activities performed on a single sport, and to avoid the current trend of using special cameras and expensive set up around the court for data collection, we developed our video dataset using sports coverage of basketball games broadcasted through broadcasting media. The detailed analysis and experiments based on different criteria such as range of shots taken, scoring activities is presented for 8 different activities using state-of-art deep learning architecture for video classification

Motion-based technology to support motor skills screening in developing children: A scoping review

Background. Acquiring motor skills is fundamental for children's development since it is linked to cognitive development. However, access to early detection of motor development delays is limited. Aim. This review explores the use and potential of motion-based technology (MBT) as a complement to support and increase access to motor screening in developing children. Methods. Six databases were searched following the PRISMA guidelines to search, select, and assess relevant works where MBT recognised the execution of children's motor skills. Results. 164 studies were analysed to understand the type of MBT used, the motor skills detected, the purpose of using MBT and the age group targeted. Conclusions. There is a gap in the literature aiming to integrate MBT in motor skills development screening and assessment processes. Depth sensors are the prevailing technology offering the largest detection range for children from age 2. Nonetheless, the motor skills detected by MBT represent about half of the motor skills usually observed to screen and assess motor development. Overall, research in this field is underexplored. The use of multimodal approaches, combining various motion-based sensors, may support professionals in the health domain and increase access to early detection programmes.Funding for open access charge: Universidad de Málaga / CBUA

Accurate and Robust Heart Rate Sensor Calibration on Smartwatches using Deep Learning

Heart rate (HR) monitoring has been the foundation of many researches and applications in the field of health care, sports and fitness, and physiology. With the development of affordable non- invasive optical heart rate monitoring technology, continuous monitoring of heart rate and related physiological parameters is increasingly possible. While this allows continuous access to heart rate information, its potential is severely constrained by the inaccuracy of the optical sensor that provides the signal for deriving heart rate information. Among all the factors influencing the sensor performance, hand motion is a particularly significant source of error. In this thesis, we first quantify the robustness and accuracy of the wearable heart rate monitor under everyday scenario, demonstrating its vulnerability to different kinds of motions. Consequently, we developed DeepHR, a deep learning based calibration technique, to improve the quality of heart rate measurements on smart wearables. DeepHR associates the motion features captured by accelerometer and gyroscope on the wearable with a reference sensor, such as a chest-worn HR monitor. Once pre-trained, DeepHR can be deployed on smart wearables to correct the errors caused by motion. Through rigorous and extensive benchmarks, we demonstrate that DeepHR significantly improves the accuracy and robustness of HR measurements on smart wearables, being superior to standard fully connected deep neural network models. In our evaluation, DeepHR is capable of generalizing across different activities and users, demonstrating that having a general pre-trained and pre-deployed model for various individual users is possible

Emotion, Content & Context in Sound and Music

Computer game sound is particularly dependent upon the use of both sound artefacts and music. Sound and music are media rich in information. Audio and music processing can be approached from a range of perspectives which may or may not consider the meaning and purpose of this information. Computer music and digital audio are being advanced through investigations into emotion, content analysis, and context, and this chapter attempts to highlight the value of considering the information content present in sound, the context of the user being exposed to the sound, and the emotional reactions and interactions that are possible between the user and game sound. We demonstrate that by analysing the information present within media and considering the applications and purpose of a particular type of information, developers can improve user experiences and reduce overheads while creating more suitable, efficient applications. Some illustrated examples of our research projects that employ these theories are provided. Although the examples of research and development applications are not always examples from computer game sound, they can be related back to computer games. We aim to stimulate the reader’s imagination and thought in these areas, rather than attempt to drive the reader down one particular path

Every sign of life

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.MIT Institute Archives copy: pages 151-[182] bound in reverse order.Includes bibliographical references (p. 142-150).Every Sign of Life introduces an approach to and motivational schema for personal health monitoring. It is an exploration of how to make information collected by personal health-monitoring devices fun and engaging, and consequently more useful to the non-specialist. In contrast to the common methodology of adding game elements to established biofeedback systems, the Every Sign of Life approach is to design and build games that use biosensor information to effect the game environment. This work tests the hypothesis that fun (the joy of learning, achieving, competing, etc.) is a way to achieve the goal of self-efficacy; to induce people to take care of their own health by altering their habits and lifestyles. One result is a basic architecture for personal health-monitoring systems that has led to an approach to the design of sensor peripherals and wearable computer components called "Extremity Computing." This approach is used to redefine biosensor monitoring from periodic to continuous (ultimately saving data over a lifetime). Another result is an approach to adding implicit biofeedback to computer games. This has led to a new genre of games called "Bio-Analytical Games" that straddles the boundary between sports and computer games. A series of studies of how to present health information to children and adults have demonstrated the ability of consumers to use bioinformatics without involving professionals.by Vadim Gerasimov.Ph.D

Less total body fat and lower extremity fat are associated with more high-intensity running during games in female university soccer players.

The purpose of this study was to investigate the relationship between body composition and in-game physical performance measures in female collegiate soccer players. Body composition measures including total mass, fat mass, and lean tissue mass, both for the lower extremities and for total body were acquired in 10 players using dual energy x-ray absorptiometry. In-game physical performance measures were collected using global positioning system (GPS) devices and included total distance covered and distance covered in 6 different speed zones. Data from fourteen regular season games were analyzed over the 1st half, 2nd half, and entire game. The level of significance was set at p<0.05. Players covered less distance in the 2nd half compared to the 1st half of the game (3356.5±1211.7m vs. 4544.7±495.2m, p=0.004). A repeated measures ANOVA revealed decreases in distance covered jogging, at low-intensity running, and at moderate-intensity running during the 2nd half compared to the 1st half of the game (p<0.001). Lower measures of total body fat mass, total body fat percentage and lower extremities fat mass were correlated to covering more distance at moderate-intensity and high-intensity running during the 2nd half and as well as the whole game (r values from -.644 to -.745, p values from <0.01 to 0.04). Our results suggest that body composition can influence the distance covered at moderate- and high-intensity running speed during competitive games. Training strategies that help reduce excess fat mass and incorporate high-intensity training bouts may be beneficial for female soccer players and contribute to overall team success

Database of Video Games and Their Therapeutic Properties

There are reported to be 2.96 billion video game players in the world as of 2021 and this number is expected to grow to 3.32 billion by the year 2024. Of that total, 215.5 million video game players live in the United States with a reported average age of 33 years old. Thousands of commercial video games are released every year. There is evidence to support video game technology use as therapeutic media however it predominately utilizes outdated technology or technology designed for a specific purpose also called “serious games.” The problem is that OT practitioners are unaware of the potential therapeutic properties of video games they have not played, so are unable to integrate unfamiliar video games as therapeutic media in clinical practice. The purpose of this capstone project is to develop an online database of commercial video games, and their therapeutic properties, to facilitate their use as therapeutic media in OT practice. To address this problem a webpage was developed in partnership with the Family Gaming Database that cataloged 10 commercial video games from commercially available video game subscription services and the Nintendo Switch. The 10 games were subject to an activity analysis based on the AMPS to determine their therapeutic potential. The resulting webpage contains three main lists in which filters can be applied in order to display games that meet a specific desired criterion. Applicable filters include platform, age rating, difficulty, and specific accessibility features. Keywords: database, occupational therapy, video game, video game

Physical fun : Exercise, social relations and learning in SuperPark

SuperParks offer a diverse set of activities, including trampolines, skate parks, obstacle walls, parkour courses, playtowers and many different games. The activities are aimed to people of all ages and fitness levels. However, the biggest group of visitors are children and young people, who make up a clear majority inside the SuperPark crowd. SuperPark describes the entertaining physical, social and mental activity inside as “sparking”. SuperPark Ltd, the company behind SuperParks, commissioned this study to investigate the quality of physical activities and social life inside the parks. The research was carried out by researchers from the South-Eastern Finland University of Applied Sciences working in Juvenia and Active Life Lab, units focusing on youth studies and preventive wellbeing, respectively. University of Jyväskylä deserves credit for providing assistance for the research. This study report consists of two parts. Part 1 focuses on the physiology of sparking and physical data collected from participants at SuperPark Jyväskylä. Part 2 addresses the social life inside SuperPark using survey and interview data collected from young visitors inside SuperPark.SuperPark tarjoaa monipuolisen joukon aktiviteetteja, muun muassa trampoliineja, skeittiparkkeja, kiipeilyseiniä, parkour-ratoja ja monia erilaisia pelejä. Aktiviteetit on suunnattu kaikenikäisille ja -kuntoisille. Suurin ja näkyvin vierailijaryhmä ovat kuitenkin lapset ja nuoret. SuperPark käyttää ”sparkkaus” -termiä kuvaamaan puistoissa tapahtuvaa fyysistä, henkistä ja sosiaalista toimintaa. SuperParkien takana oleva SuperPark Oy tilasi tämän tutkimuksen saadakseen tutkimustietoa fyysisen aktiivisuuden ja sosiaalisen elämän laadusta puistoissa. Tutkimuksen toteuttivat Kaakkois-Suomen ammattikorkeakoulun tutkimusyksiköissä, nuorisoalan tutkimus- ja kehittämiskeskus Juveniassa ja tietoon perustuvia hyvinvointipalveluja kehittävässä Active Life Labissa, työskentelevät tutkijat. Jyväskylän yliopisto ansaitsee kiitoksen avusta, jota se tarjosi tutkimusprojektille. Tämä tutkimusraportti koostuu kahdesta osasta. Osa 1 käsittelee sparkkauksen fysiologiaa ja hyödyntää SuperPark Jyväskylän koehenkilöiltä koottua fyysistä mittausaineistoa. Osa 2 kuvaa sosiaalista elämää SuperParkeissa nuorilta Super-Park-vierailijoilta kootun verkkokysely- ja haastatteluaineiston avulla