| Summary: | The aim of this study is to increase the depth of data available to swimming coaches in order to allow them to make more informed coaching decisions for their athletes in front crawl swimming. A coach’s job is to assist with various factors of an individual athlete to allow them to perform at an optimum level. The demands of the swimming coach require objective data on the swim performance in order to offer efficient solutions (Burkett and Mellifont, 2008). The main tools available to a coach are their observation and perceptions, however it is known that these used alone can often result in poor judgment. Technological progress has allowed video cameras to become an established technology for swim coaching and more recently when combined with software, for quantitative measurement of changes in technique. This has allowed assessment of swimming technique to be included in the more general discipline of sports performance analysis. Within swimming, coaches tend to observe from the pool edge, limiting vision of technique, but some employ underwater cameras to combat this limitation. Video cameras are a reliable and established technology for the measurement of kinematic parameters in sport, however, accelerometers are increasingly being employed due to their ease of use, performance, and comparatively low cost. Previous accelerometer based studies in swimming have tended to focus on easily observable factors such as stroke count, stroke rate and lap times. To create a coaching focused system, a solution to the problem of synchronising multiple accelerometers was developed using a maxima detection method. Results demonstrated the effectiveness of the method with 52 of 54 recorded data sets showing no time lag error and two tests showing an error of 0.04s. Inter-instrument and instrument-video correlations are all greater than r = .90 (p < .01), with inter-instrument precision (Root Mean Square Error; RMSE) ≈ .1ms−2, demonstrating the efficacy of the technique. To ensure the design was in line with coaches' expectations and with the ASA coaching guidelines, interviews were conducted with four ASA swim coaches. Results from this process identified the factors deemed important: lap time, velocity, stroke count, stroke rate, distance per stroke, body roll angle and the temporal aspects of the phases of the stroke. These factors generally agreed with the swimming literature but extended upon the general accelerometer system literature. Methods to measure these factors were then designed and recorded from swimmers. The data recorded from the multi-channel system was processed using software to extract and calculate temporal maxima and minima from the signal to calculate the factors deemed important to the coach. These factors were compared to video derived data to determine the validity and reliability of the system, all results were valid and reliable. From these validated factors additional factors were calculated, including, distance per stroke and index of coordination and the symmetry of these factors. The system was used to generate individual profiles for 12 front crawl swimmers. The system produced eight full profiles with no issues. Four profiles required individualisation in the processing algorithm for the phases of the stroke. This was found to be due to the way in which these particular swimmers varied in the way they fatigued. The outputs from previous systems have tended to be either too complicated for a coach to understand and interpret e.g. raw data (Ohgi et al. 2000), or quite basic in terms of output e.g. stroke rate and counts (Le Sage et al. 2011). This study has added to the current literature by developing a system capable of calculating and displaying a breadth of factors to a coach. The creation of this system has also created a biomechanical research tool for swimming, but the process and principles can be applied to other sports. The use of accelerometers was also shown to be particularly useful at recording temporal activities within sports activities. Using PC based processing allows for quick turnaround times in the processing of detailed results of performance. There has been substantial development of scientific knowledge in swimming, however, the exchange of knowledge between sport science and coaches still requires development (Reade et al. 2008; Williams and Kendall 2007). This system has started to help bridge the gap between science and coaching, however there is still substantial work needed. This includes a better understanding of the types of data needed, how these can be displayed and level of detail required by the coach to allow them to enact meaningful coaching programmes for their athletes.
|
|---|
