STROKE-BY-STROKE ACCELERATION ANALYSIS OF AN ELITE SWIMMER Benoit LUSSIER*, Mathieu CHARBONNEAU, François BILLAUT Institut national du sport du Québec (Montréal, Canada) Introduction Block Flight Dolphin kicks Free swim Glide The IoC_arms increased a little bit through the race. Also, the strategy of kicking slightly before the opposite arm (i.e., IoC_arm/leg) appeared to be a more efficient technique for this swimmer, because the efficiency was better when the IoC_arm/leg was lower. The methods currently available to analyse performance in swimming rely on a few strokes sampled from the middle of each lap or on an average score of the laps1. These methods only provide a performance snapshot, and may offer limited information as to the development of neuromuscular fatigue or other technical information during a swim. The purpose of this study was to use an accelerometer to conduct a stroke-by-stroke analysis of a 100-m freestyle swimming performance. Materials & Methods (ms) Arms contribution decreased over the swim, whereas legs contribution to forward acceleration increased. The acceleration signal was recorded (Nanotrack, Catapult) from an elite sprint swimmer of the National Training Center in Montréal during an all-out 100-m swim, and further divided into swimming phases: block, flight, glide, dolphin kicks, free swim, and turn around2. The signal was band-pass filtered to remove signal noise and gravity. A Fast-Fourier transform was performed on an entire lap of free swim and frequency peaks for the arms and legs were identified. The signal was then band-pass filtered on each peak in order to isolate the respective acceleration produced by arms and legs. We also calculated the following variables for every stroke: stroke rate (SR, stroke/min), stroke length (SL, m/stroke), forward efficiency (RMSE between forward acceleration and resultant acceleration for every stroke), an index of coordination for the arms (IoC_arms, latency between each effective stroke), and an IoC_arm/leg (timing between each effective arm stroke and the closest effective kick). A FFT was performed on the raw forward acceleration signal. The peak at 0 hz represented the gravity. The next important peak was at 1.6 hz, which represented the frequency of the arms and the last one was at 4.8 hz, which represented the frequency of the legs. Those 2 facts where validated with a video analysis. Conclusion This stroke-by-stroke processing of the acceleration signal proved a ROBUST and SENSITIVE tool to analyse freestyle swimming performance (see companion poster: Charbonneau et al. SPIN Summit 2013). It has to be noted that the software is already functional to analyse backstroke and butterfly. This tool could help coaches and sport scientists examine fatigue development in lower- and upper-limb muscles, training adaptations and pacing strategies, as well as track changes in technique. 4.3-5.1 hz : legs 1.3-1.8 hz : arms SR and SL were calculated with the zero crossing of the acceleration data of the arms. SR decreased while SL increased during the swim. Results References Each swimming phase was clearly visible on the signal. The first phase is the block phase, which is the initial push off the starting block. The swimmer had a lag time between the pushes of the 2 feet and it was visible on the raw acceleration signal. After the last toe quit the block, there is a short aerial phase. Then, the swimmer enter the water and the shock created a non constant acceleration signal. Finally, the swimmer did 6 dolphin kicks before he started to swim. The different phases were cut when the acceleration signal crossed a virtual line at 0 m/s2. 1. Chollet et al. Int J Sports Med, 2000; 21: 54-59. 2. Bachlin et al. Perv Mob Comput, 2011; 8: 68-81. After band-pass filtering, accelerations of the arms and the legs were isolated from the raw signal. Calculations were conducted with these data. blussier@insquebec.org Presented at SPIN Summit 2013, Calgary