An investigation into motion analysis technology and its influence in sport
Chris Keaveney – N00162019
Background & Past Technologies
Motion analysis has been more & more significantly apparent in sport over the past decade and with good reason. It is essentially a way of giving accurate motion capture systems for the sports biomechanics market. This can be done to measure an individual’s biomechanics in a specific sport or an entire team play action. As mentioned in the review written by Sian Barris & Chris Button, player motion tracking had traditionally involved a wide variety of data gathering methods in which either during a game or after, video analysis is used to manually gather this data.  The main method used was notational analysis. This is the study of movement patterns, strategy, and tactics in professional team sports.  Notational analysis generally focused on smaller numbers of players in pre-defined areas. As a result of this, it was not as efficient but did prove to be an easier & less costly method. Also despite notational analysis being a cheaper & easier method, the accuracy of the process may be different depending on certain processes which would most definitely hinder its reliability.
This would include the number of spectators actually being used, their experience & the quality of their viewing perspective. It is also made evident in this piece (Barris & Button, 2008, pp. 1025) that the progress of keeping track of teams in sports using automated tracking tech was hindered as a result of the lesser quality of computer & video services present in sporting venues. It is clear that there is some justification in the lack of progress with automated tracking however because of the unpredictable movement & collisions of athletes in sports which “violate the assumptions of smooth movement on which computer tracking algorithms are typically based” (Barris & Button, 2008, p. 1025).
Some commercial tracking systems which give a resulting analysis that is used by coaches & athletes are TRAKUSÔ, SoccerManÔ, TRAKPERFORMANCEÔ, Pfind, rÔ and ProzoneÔ. All of which require a certain amount of controlling an from external operator. According to Barris & Button this is to “process the data after capture and are often limited by the restricted capture environments that can be used and the necessity for individuals to wear tracking devices“ (Barris & Button, 2008, p. 1026). This accounts for one of the many evident complexities which surround the idea of tracking for motion analysis in sport. As it stands there is not yet a completely automated system for motion analysis sports performance that is commercially available. Despite automatic motion tracking being successfully used for surveillance in the military/security industry, motion analysis in sport requires a system which can clearly and consistently identify & keep track of many interconnecting players in a condensed environment. This is emphasized by “the quality of video capture, the relative size and occlusion frequency of people, and also changes in illumination“ (Barris & Button, 2008, p. 1026). Alternative automated motion detection methods which could be used are organizing schemes, gathering feedback, recording the organization of the team being tested along with any other “objective measures of intervention effectiveness in team sports, which could benefit coaches, players, and sports scientists“ (Barris & Button, 2008, pp. 1026). According to (Barris & Button, 2008, p. 1026), gathering correct information on the positioning of athletes in sport is intriguing to coacheshigh-performancemance sportspeople because of the possibility to associate performance to tactics and also help with the creation of improved training programmes.
This data allows sports scientists to “understand the coordination dynamics of player activity and the most influential constraints acting upon them“ (Barris & Button, 2008, p. 1026). An example of this would be when this data is used to determine the velocity/acceleration of certain players in a match and to compare their unsuccessful and successful games. This most definitely had changed the world of sport as it had proved to be useful to determine player patterns and also build a description of a certain player recording their typical characteristics in a game with accuracy. Although these advances in motion analysis technology are quite obviously apparent, there sremainmains numerous obstaclesn a in automatic player tracsystem king with motion analysis.
Motion analysis has been applied for over 30 years to the study of work rates in professional sccer, since the classical study of Reilly and Thomas. Curr”,ently soccer represents the most commanalyzedlysed sport in motion analysis technology, it (and some racket sports) show the greatest technological developments and they demonstrate significant differences in player numbers, patterns of play and size of capture environment (Barris & Button, 2008, p. 1027). This is why I will be using these sporting examples primarily in this paper. Unfortun”,ately Few systems have the abilitanalyzealyse all the players in a team throughout a whole match, tracking each player both on and off the ball. This shows that despite the quality of motion analysis technologies available, there is still a large margin for development. Motion analysis in sport is there for shown to be an ever-growing influence in sport improving every year.
The AMISCO Pro® 1 system by Sport-Universal Process in collaboration with the French Football Federation was developed in1990s990’s. This was the first system to achieve the simultaneous analysis of the work rate of every player in a team throughout the entirety of a match. it did this by measuring the movements of every player on video including the referee and the ball. It will record activity 25 times per second during the entirety of the match. [12″,16] Roughly 4.5 million data points for position on the pitch with more than 2000 ball touches per match are gathered from this. The AMISCO pro & its’ main European competitor The ProZone® System both pioneer multi-player video tracking systems based on top of the range video and computer technologies are currently the most commonly used and comprehensive commercial tracking systems in professional European soccer. The systems in question supply a detailed analysis of each player’s work rate over a whole match played, and make a 2d animated reconstruction of the movements of players with an interactive graphical representation of all playing actions such as passes and duels.
Multiple player video tracking systems like the AMSISCO Pro & ProZone involve a layout which consists of multiple cameras being fixed in optimally calculated positions to cover all of the surface ay, and capture all player movements.  Although these systems are mostly computer automated, occasionally an operator must correct certain mistakes manually. For example, when motion tracking becomes impossible due to changes in light quality. Despite, this the Dvideo® system designed at the University of Campinus, Brazil, is reported to have a 95% automatic tracking rate. Although this system uses a lower number of digital film images per second (7.5 Hz) compared with other video tracking systems such as AMISCO Pr
Real-time time analysis is now available with the latest commercial video-based automatic tracking systems DatatraX® and the TRACAB® image tracking system During matches Three manual operators need to manage the whole process. two people to fix tracking errors in real-time for each team and one to do a voice recognition coding procedure. These systems both influence sport in a significant way as they serve coaches and trainers with a large amount of immediate informaabouton of current match performance. This in return allows these people to make informed decisions mid-game which could most definitely influence the outcome of the match in question and has influenced modern sport as we know it today.
As stated by (Di Salvo V, Collins A, McNeill B), one highly significant advantage of manual and automatic video-based tracking systems is the fact that they do not need players to carry any electronic transmitting device which is strictly forbidden in most sports. Their disadvantage lies with expensive costs & installation of a computerized network with “at least one dedicated operator to organize the data collection and further operators to perform the analysis“ & multiple cameras of course. 
The LPM Soccer 3D® system developed by INMOTIO in association with PSV Eindhoven Football Club gives position measurements at more than one hundred times per second which has resulted in new, very detailed info about player accelerations, decelerations & changes in direction.  The system itself also puts physical data together with physiological measurements with heart rate monitoring. It also uses synchronized video footage for a complete picture of the daily, weekly and monthly workloads experienced in training  (p. 845 paragraph 1). There are negativities to systems such as these however such as possible electronic interference and the energy source required to successfully do this signal transfer.  Additionally there has not yet been a scientific investigation into the reliability of these electronic measuring systems.
GPS technology has also started to influence motion analysis in sport. Like electronic transmitting devices, it is only permitted to recording the efforts of players during training or friendly matches. Despite this, it is now allowed to be usecompetitiontiton in Australian rules football.
Drawbacks of current technologies
According to (Carling et al., 2008, p. 846) the main problems associated with the current technologies in motion analysis sporting are firstly validity, objectivity & reliability. The second problem he acquainted was the interpretatiothe n of results obtained. Referring to validity, objectivity & reliability, it is made evident that The methods used to gather motion analysis data have to meet these three requirements for scientific criteria for quality control.  It is known that many of the commercial motion analysis systems mentioned previously in this paper have not completed sufficient quality control checks. Besides the statements by manufact”,urers there is very little scientific proof to verify validity claims. These difficulties are primarily attributed to researchers encountering logistical issues such as obtaining access to test systems using playing facilities within soccer stadiums.
The second drawback of current technologies within motion analysis in sporan t is interpretation of results. In the majority of sports today, motion analysis technologies such as those mentioned in this piece are used to obtain data associated with occurrences in a match and physical effort of players. With physical efforts of players, their efforts are taken/calculated from the data collected from speed, distance and time. This can be problematic when using distance and require a far more detailed approacanalyzingysing the data received. This is because the methods used falsely assume that the player is using energy only when moving to a new location on the pitch. It does not take many factors into account such as the energy expended when heading the ball for example. This problem is quite a drawback in today’s technologies as it actually causes underestimation of the entire energy expended by the players on a team without clear changes of location on the pitch.
Future Research & Conclusion
Regarding future research in motion analysis technology in “,sport there are many obstacles which present themselves today making progress in this field a challenge. The complex nature of modern sport is an evident reason for this. The current task is to achieve a system which can clearly and consistently identify & keep track of many interconnecting players in a condensed environment over a long period of time. Despite, this there are currently systems which are being developed to successfully do this but they are primarily for security and surveillance purposes. Referring back to (Barris & Button, 2008, p. 1026) They do not take into account “the quality of video capture, the relative size and occlusion frequency of people, and also changes in illumination“ which are all key components in an outdoor sporting match. Despite, this in recent years thavee has been significant developments in obtaining fully automated human motion tracking and reconstruction. These developments mainly focus on tracking multiple people in unstructured outdoor environments, the reconstruction of human movement from many different views, monocular motion reconstruction, pose estimation in natural scenes and the understandinbehaviorviour and actions.[7″,15”,19]
Future developments focus on the detection of body parts, the movement of clothing and body shape. This would without a doubt benefit tracking reliability and pose estimation in clutter scenes. The benefits of a fully automated motion tracking system that could record and scale data on match players would significantly help the sporting community. An example of this would be in sports such as basketball or handball where intricate data on real-time player information sucthe h as number of passes made, speed, distance covered etc. would be highly beneficial to quantify successful and unsuccessful plays. Not only would the information derived from an automated tracking system benefit coaches, but also exercise & scientific researchers. (Barris & Button, 2008, pp. 1041) also mention that “As well as being of benefit to coaches and athletes, sport and exercise science researchers would benefit from information derived from an automated player tracking system.“ Here they refer to the ability for sports psychologists to learn about team interactions and group dynamics/performances.
In conclusion, It is clear that despite many obstacles which present themselves in motion analysis and the complex nature of the subject itself. Motion analysis remains a crucial aspect of modern sport and has shown to benefit the overall evolution of how athletes, teams, resear, hers and sports themselves can grow and improve. The challenges presented also provide a goal for many sports influencers to strive towards, changing the direction of match and athlete quality as we know it today and for the distant future.ever-growingowing ability to gather video data images has made it possible to improve sporting analytics and procedures. Despite the manual traditional methodology of notational analysis btime-consuminguming and somewhat tedious, the current computer technologies & methods which have evolved from such tactics have proven to be quite effective and present many opportunities for the improvement of sport as it is today.
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