Tom 17 (2018): Zeszyt 1 (July 2018)

This article discusses the development and application of virtual environments (VEs) in the domain of exercise as well as research in recreational and high-performance sports. A special focus is put on the use of virtual characters (VCs). For its elaboration, the following criteria parameters were chosen: scene content and the role of the VC, output device, kind of additional feedback, level of expertise of the tested participants, kind of user’s movement (reaction), kind of the visualization of the user’s body, kind of study and kind of evaluation. We explored the role of VCs embodying virtual opponents, teammates, or coaches in sports. We divided these VCs in passive and autonomous characters. Passive VCs are not affected by the user, whereas autonomous VCs adapt autonomously to the user’s movements and positions. We identified 44 sport related VEs, thereof 22 each in the domain of recreational sports and high-performance sports: of the identified 44 VEs, 19 VEs are without VC, 20 VEs with passive VCs, and 5 VEs with autonomous VCs. We categorized studies examining expert athletes in high-performance sports as well as studies analyzing novices, beginners or advanced athletes in recreational sports. Nevertheless, all identified systems are suitable for athletes of recreational and high-performance level

To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day. Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were then generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC<0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting limited application as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training observations may improve predictive models for injury prevention

Recently there has been an interest in developing tennis scoring systems that involve playing a fewer number of points on average. In devising such ‘shorter’ tennis scoring systems, it would be ideal for them to also have the following four characteristics: A smaller standard deviation of duration, a similar value for the probability that the better player wins, an increased efficiency, and a greater average excitement per point played. Thus, in total there are five considerations when devising such new scoring systems. Quite often in this type of study a scoring system that is ‘better’ with regard to one of these characteristics is ‘worse’ with regard to another (or others). In this paper we outline some new tennis scoring systems that have improvements in all (or almost all) of these five characteristics. We identify 3 or 4 different game structures that could be useful for tournaments. A common thread in the approach taken is the elimination of unimportant and unexciting points within the game structure. The choice of which is the most appropriate new format for a particular tournament would depend amongst other things on the planned reduction in the expected set duration

The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions