Introduction
Success in sports-related activities depends upon the understanding of the performer-environment system. Historically, these competences have always been based on accumulated expert opinions and scarce science, limited to laboratory-based studies with poor ecological validity. The technological revolution has made available a large spectrum of continuous data about human movement activities, throughout the use of non-invasive, portable, and relatively affordable sensors (e.g. GPS, GNSS, UWB, accelerometer, gyroscopic, magnetometer).
The increase in computational power & data availability over the last years now allows mathematical frameworks and advanced scientific methods to assess human behaviour from a complex system paradigm. In brief, ‘complexity science’ studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit emergent global structures and behaviours at larger scales.
The real-life applications of complexity-based research can provide, for example, different understandings of motor learning and help to monitor how individuals develop (or recover) motor skills. They can also provide tools to identify synergies in team sports and upgrade current knowledge of collective behaviour. In the end, practitioners will be dealing with high-quality processed information to be used in the most frequent decision-making processes related to optimizing sports performance and health.
Research - industry transfer
The process of transferring research outputs in sports sciences that deal in particular with technological-derived developments has to be done with caution. In the short-term, it will most likely result in slowing down all systems, rather than improving them. Therefore, a large amount of pilot-testing and scenario simulation is needed to identify all possible problems and establish a stronger sense of trust between all parts.
The process of transferring technology is often focused on searching for the best solutions to problems related to the used hardware (validity of measurements at very high-speeds, improvements in portability and comfort, battery duration, speed of transmission and downloading data, real-time access to data, etc.), but also fine improvements in the software (filtering data, processing capabilities and user-customization, alarm thresholds, visualization methods and fast deliverance to different types of users, etc.). Simultaneously, the collaboration to produce new knowledge using these systems is also very frequent (e.g. descriptions of the physiological or biomechanical impact of specific training programs, testing of new manipulations in training tasks, and development of new variables for tactical match analysis).
In teams’ sports settings, for example, research has been providing instruments and procedures that enable practitioners to monitor day-to-day physical workloads of elite and younger players, aiming to optimize performance and better control the risk of injury. In football, it is well-known that most elite teams present substantial losses due to injury-related decrement in performance per season, and thus, the clubs have a strong economic incentive to invest in injury prevention and rehabilitation programs.
From a tactical perspective, state-of-the-art research has also been providing new tools to analyze player activity using “x” and ”y” field-positioning data. This is currently one of the sources of key information that, after adequate processing, allows to reveal how players behave in interaction with teammates and opponents and have different levels of contribution to the expression of successful collective behaviour. This is also a topic that offers new objective information to improve the process of team performance optimization, but also the process of talent recruitment and development is relevant, allowing to upscale elite organizations to a more sophisticated and effective level of decision-making.
In the end, all partners involved try to contribute to an overall solution with the ability to develop personalized models that are built and constantly improved with individuals’ past data instead of being a “one size fits all”. These models have to be multidimensional and able to visualize processed information from physical, physiological, technical, and tactical perspectives.
Opportunities
A practical example of the opportunities in this market in the sports industry is the WIMU Pro wearable player tracking system. Their system embeds GPS and UWB (Ultra-wideband) technology to facilitate data collection. The software, on the other hand, includes live tracking, post session analysis and cloud services to promote data sharing. Their approach aims at quantifying human performance and, thereby, answering the key performance questions of teams and athletes. They collaborate with research to make sure that their products become validated and are up to date with the newest insights and technologies.
There is still much work to be finished in the sports performance area related to team sports, either from the technical or application points of view, such as the processes of sensorization of the ball, the incorporation of 3-D analysis in vertical sports like volleyball or basketball, or the synchronization with other technology capable of automatically recognizing technical and tactical actions. Nevertheless, the forthcoming opportunities to develop innovative work using sensors that track human movement activities might also be related to the expansion into different fields of application. One of the most promising approaches is the enrichment of solutions under the domains of exercise and health.
Besides the obvious and already well-known applications that control the daily workloads and energy expenditure, the availability and easy access to continuous high-frequency data about human movement might allow, for example, to develop new processes to access movement variability when walking and then try to identify how the data might be used to inform about degeneration in the aging process, the risk of falling, and consequent incapacitating injuries.
From another perspective, the activity at work seems also a promising field to explore as not only the health risks caused by sedentary behaviour are important, but also the other problems caused by postural errors and repetitive movements. Therefore, it seems very promising to diagnose the activity profiles of different professions and help mitigate the health risks of repetitive movements that certainly cause repetitive strain injuries.
Along with all these new possible developments, there seems to be a need to bring up the topic of excessive monitoring and its potential detrimental effects on human behaviour, but also the possible paralysis by analysis effect. Therefore, the future must certainly envisage the contribution of embedded heuristic solutions to sophisticated technology to ensure that the process runs smoothly.
Conclusion
Current state-of-the-art has reached levels of specialization that require the collaborative work of multidisciplinary teams. Therefore, an important part of the current and future challenges requires not only a holistic view of sports performance and health as presented by complexity science, but also the adequate and corresponding principles of data collecting, processing, visualization, and integrative communication. The universities and the industry are constantly collaborating with the aim of fine tuning the hardware, software, and producing new knowledge that can help the work of practitioners.
Although the process of using large amounts of sensor-based data in the decision-making process of (team) sports is still in its infancy, it is already embedded in elite clubs and gradually incorporating the daily activities in all scenarios. Still, it must never be forgotten that technology might be part of the answer, but always remains under the track of relevant questions impregnated by humanistic views and approaches.
Therefore, we should aim towards a future where complex problems are solved with the help of integrated technology which contributes to data-informed decisions, instead of imposing data-driven solutions.
Working on tracking technologies yourself from an academic, industry or sports field application point of view? Let us know. We're happy to create some more bridges in this area!
This article has been written by Jaime Sampaio, the Pro-Rector for Infrastructures and Scientific Projects at the University of Trás-os-Montes e Alto Douro, Portugal.
