Determining maximum push-off velocity in swimming using accelerometers

Transcription

1 Available online at Procedia Engineering 60 ( 2013 ) th Asia-Pacific Congress on Sports Technology (APCST) Determining maximum push-off velocity in swimming using accelerometers Andy Stamm a*, Daniel A. James a, Brendan B. Burkett b, Rabee M Hagem a, and David V. Thiel a a Centre for Wireless Monitoring and Applications, Griffith University, Brisbane, Australia b School of Health and Sport Sciences, University of the Sunshine Coast, Maroochydore, Australia Received 25 April 2013; revised 23 May 2013; accepted 23 May 2013 Abstract The ability to unobtrusively measure velocity in the aquatic environment is a fundamental challenge for engineers and sport scientists. In the past video systems were the first choice to monitor the athletes during training. With the acceptance of small inertial sensors new ways of investigating the athletes movements have arisen. These small systems are light, easy to use and do not hinder the athlete during training, allowing an easy tracking of training improvements. This research used a self developed low-cost inertial sensor system, attached to the lower back, to record the acceleration of swimmers performing sub-surface wall push-offs. The recorded data were downloaded to MATLAB for data analysis. Using seven swimmers, the wall push-off velocity at three different efforts was analysed and compared to a tethered cable velocity meter (criterion measure) using Regression and Bland-Altman analysis. The maximum velocity determined from the accelerometer unit was linearly related to the benchmark criteria (r 2 =0.89). A biomechanical technique for the determination of the maximum glide velocity from acceleration data has been verified The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection 2013 Published and peer-review by Elsevier under responsibility Ltd. Selection of and the School peer-review of Aerospace, under responsibility Mechanical and of Manufacturing RMIT University Engineering, RMIT University Keywords: Swimming; push-off velocity; accelerometers * Corresponding author. Tel.: address: andy.stamm@gmail.com The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection and peer-review under responsibility of the School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University doi: /j.proeng

2 202 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) Introduction 1.1. Background The velocity of swimmers in training and competition is of major interest to coaches, not only as an assessment and comparison tool, but also to monitor training load [1-3]. In the pool, accelerometers can provide accurate stroke rate and lap times [4], real time feedback using optical communication [5] but the calculation of instantaneous swim velocity has not been achieved. The assessment of velocity from a push-off and glide using a small, low cost, portable accelerometer will be of significant benefit for the understanding and future performance of the sport, and an important step in determining intra-stroke velocity using a standalone wearable sensor. The push-off from the wall of the pool allows the swimmer to reach velocities much greater than can be achieved in the free swim phase and also allows time for the swimmer to rest the upper body [6-7] Acceleration & velocity The sensor used in this research records three channels of acceleration. The x-channel represents the mediolateral, the y-channel the forward and the z-channel the anterior-posterior direction. The total acceleration was calculated using: a tot ( t) 3 i 1 a ( t) i 2 (1) where t represents the time, i = x, y, and z, and a tot the total acceleration as time series data. The velocity at a particular time v(t) was calculated using an approximation (trapezoidal rule) to the numerical integration of the acceleration a tot (discrete data) and can be calculated using: a( t) a( t 1) v ( t) v( t 1) t (2) 2 where v(0) = 0, a(t) and a(t+1) are adjacent acceleration values and the time between two samples can be represented by t (1/sampling rate of the sensor). Calculations using discrete accelerometer data has potential but may introduce errors: The acceleration data are contaminated by changes in the orientation of the sensor causing baseline changes due to the gravitational acceleration. The inherent noise level in the acceleration units results in additive noise so that as t 2 t 1 increases, so the cumulative error in velocity increases. 2. Methods 2.1. Instrumentation This study used a custom designed, low cost inertial sensor [8], which consists of a tri-axial accelerometer (±6 g). The sensor is capable of recording up to one hour of tri-axial acceleration data at a sampling frequency of 100Hz. Figure 1 shows the sensor with the dimensions of 69 x 28 x 7 mm and a mass of 15 grams including the battery. The operating platform is a low power embedded Real Time Operating System (RTOS) developed by Wixted et al [9].

3 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) Figure 2 shows the position of the sensor and the coordinate system for the three axes. The y-channel measures negative acceleration into the swimming direction, which was considered for data interpretation. y x Fig. 1. (a) Custom developed low cost inertial sensor and its dimensions. (b) Taped accelerometer unit at the lower back of the swimmer. The coordinate system shows that the swimmer gliding in the y direction. The most commonly used method to quantify velocity is by video analysis, or to use a tethered velocity meter. Video camera systems are popular [10-12] as they do not hinder swimmers during the performance, but this process has the disadvantages of (a) a complex and time consuming setup, (b) inaccuracy in measurement due to unrecognisable reference points caused by bubbles or water turbulences, (c) the parallax error and (d) a time consuming process to digitise the video footage. The last point is one of the major problems using this technology and does not allow real-time processing. Tethered velocity meters can provide an accurate measure [13-14], however this technique is limited to one lap of the swimming pool. This study aims to compare accelerometer derived push-off velocities to a reference measure. As video systems usually capture the video footage with 25 frames per second, the authors decided to use the velocity meter as a reference system as it provides a higher sampling rate and therefore a more precise detection of the maximum push-off velocity. Using the method developed by Craig et al. [13] the velocity meter (SpeedProbe 5000 SP5000 [14] as close as possible to the sensor (Figure 1(b)) Swimmer selection & glide details Seven male swimmers participated in this research (Table 1). No prior injuries were reported. The research. After a self-determined warm up, the inertial sensor was attached to the swimmers lower back and the SP5000 tether to the swimmers costume. Following the method used by Craig et al. [15], the swimmers were asked to use their feet to push-off, and once in the glide position, to remain in the same relative body position until out of breath or no longer moving forward. Every swimmer performed 12 subsurface wall push-offs at three different effort levels (slow, medium and fast). Due to technical difficulties with two consecutive push-offs, the total number of push-offs (n=84) were reduced by two (n=82).

4 204 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) Velocity calculation The acceleration data was high-pass filtered using a Hamming windowed finite impulse response (FIR) filter with a frequency of 0.5 Hz to remove the sensor orientation from the acceleration data. The use of 0.5Hz as the cut-off frequency was chosen as the push-off has a time duration of only 0.5s (2Hz). The filtered total acceleration was calculated from the filtered acceleration data (x, y and z) using Equation 1, to find the magnitude of the swimmers acceleration (Figure 2(b)). As the filter removed the data were applied. The velocity of the swimmer was calculated using the filtered total acceleration data substituted into Equation 2. The determination of the start and stop times for the push-off were undertaken using the unfiltered three channel acceleration data and the total acceleration calculated from the unfiltered data (Figure 2(a)). The total acceleration shows significant power in the acceleration signal (values above 1 g) for the duration of the push-off phase (t 1 - t 2 ). The start of the push-off (t 1 ) was identified from the point where simultaneously; the total acceleration had a local minimum before it starts to increase above 1 g, where the z-channel had a local maximum before it starts to decrease (a z < -1 g) and the y-channel acceleration starts to rise (a y > 0 g). The end of the push-off (t 2 ) was identified from the point where the total acceleration returned to 1g and had a local minimum and the y- and z-channel had a local maximum after returning from a negative acceleration peak. (a) (b) Fig. 2. Accelerometer data showing one glide. (a) Subplots are the x, y, z and total acceleration measurements and the start (t 1) and end (t 2) of the push-off. (b) y and filtered total acceleration measurements for the push-off phase. Lowest plot shows the velocity meter velocity (solid line) and the sensor calculated velocity (dashed line) and the vertical line represents the end of the push-off (t 2). The swimmer decelerates throughout the glide after the end of the push-off (t 2 ). The total acceleration always shows a positive value due to the method of calculation, therefore it must be treated as negative to calculate the velocity reduction throughout the glide. The algorithm was written in MATLAB (Version , The MathWorks, Massachusetts, USA). 3. Results to the 1g acceleration of the e a tot is 0g, there is no acceleration apart from the gravitational acceleration. The push-off takes place in the time between 0 s to 0.68 s followed by the glide which shows the swimmer in a horizontal position (a z = -g). The swimmer decelerates (a y < 0) throughout

5 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) the glide. The termination of the glide is evident when the swimmer changes from a horizontal position (a y > 0). The scaled acceleration data, the velocity meter and sensor calculated velocity is presented in Figure 2 (b). The integration of the filtered total acceleration over this period results in a terminal velocity of v = 1.77 m/s. This maximum velocity (dashed line) is in close agreement with the SP5000 maximum velocity (solid line), which was measured as 1.73 m/s. Table 1 presents all swimmers with their experience, age, average detected velocity and the slope of the trend line. Table 1. Overview of all swimmers and their experience, age, average detected velocity and the calculated slope of the trend line after regression analysis were undertaken. Swimmer Experience Age Average detected velocity (%) Slope 1 Recreational swimmer Recreational swimmer Recreational swimmer International competition National competition National competition Recreational swimmer (a) (b) Fig. 3. Results of the regression analysis of Speed Probe 5000 (SP5000) vs. sensor derived velocity of (a) each swimmer individually and (b) all push-offs (n=82) as one data set (r = 0.94, p<0.0001). Regression analysis was used to find the correlation between the maximum sensor velocity and the maximum SP5000 velocity for each individual subject (Figure 3 (a)) and for the whole data set (Figure 3 (b)). As both technologies measure the same parameter, the point (0,0) was included as a data point for each individual swimmers data set. The linear relationship between the measured SP5000 and calculated sensor velocities for all push-offs (n=82) is very strong (r=0.94, p<0.0001), furthermore the average pushoff velocity ± standard deviation was 2.46 ± 0.50 m/s and 2.31 ± 0.52 m/s for the SP5000 and sensor velocity respectively. The average calculated sensor velocity was therefore 93.1% of the measured SP5000 velocity. These results are similar to maximum velocities found by other researchers [16-17].

6 206 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) Discussion Bland-Altman analysis was used to investigate the agreement between the two methods in determining the velocity for all push-offs (n=82). Figure 4 (a) shows the average difference between the two measurement techniques (bias) of m/s and the 95% limits of agreements of 0.15 m/s and m/s for the upper and lower limit. The scattering around the bias is even, which indicates that there is no systematic measurement error between the two methods. The scattering around the bias is also independent from the push-off velocity, which shows that the sensor derived velocity is independent of the push-off velocities (different efforts). Figure 4 (b) shows that the scattering around the bias follows a normal distribution with a skewness of 0.22 and kurtosis of 3.86 (normal distribution has a kurtosis of 3). (a) (b) Fig. 4. Results Bland-Altman analysis (a) showing the bias of m/s and the 95% limits of agreement (0.15 m/s upper and m/s lower limit) and (b) that the scattering follows a normal distribution with a skewness of 0.22 and kurtosis of The aim of this study was to validate the use of a lower back mounted inertial sensor for the determination of push-off velocities. This method was compared with a velocity meter derived velocity (criterion measure). Nearly perfect correlations (r = 0.94), a slightly lower bias (-0.15) and a low error of estimate (0.15) show that a single inertial sensor is a valid method of measuring a swimmers push-off velocity. Other research has shown that inertial sensors are capable of measuring velocity characteristics [18] and detect stroke phases [19]. This is the first study to our knowledge which has used inertial sensors to calculate push-off velocity. The use of filtered total acceleration data were sufficient to eliminate the change in the tilt of the accelerometer units at the lower back and to reduce the error level allowing an accurate determination of the velocity as a function of time. The maximum push-off velocity can be calculated from acceleration data acquired from the lower back alone. Acknowledgements The authors would like to thank the swimmers who participated in this study.

7 Andy Stamm et al. / Procedia Engineering 60 ( 2013 ) References [1] G. J. Wilson, R. U. Newton, A. J. Murphy, and B. J. Humphries, "The optimal training load for the development of dynamic athletic performance," Medicine and science in sports and exercise, vol. 25, pp , [2] M. Avalos, P. Hellard, and J. C. Chatard, "Modeling the Training-Performance Relationship Using a Mixed Model in Elite Swimmers," Medicine & Science in Sports & Exercise, vol. 35, pp , [3] A. Stamm, D. Thiel, B. J. Burkett, and D. A. James, "Roadmapping performance enhancement measures and technology in swimming," The Impact of Technology in Sport II, pp , [4] N. Davey, M. Anderson, and D. A. James, "Validation trial of an accelerometer-based sensor platform for swimming," Journal of Sports Technology, vol. 1, pp , [5] R. M. Hagem, D. V. Thiel, S. G. O'Keefe, A. Wixted, and T. Fickenscher, "Low-cost short -range wireless optical FSK modem for swimmers feedback," IEEE Sensors, 2011, pp [6] H. Shimadzu, R. Shibata, and Y. Ohgi, "Modelling swimmers' speeds over the course of a race," Journal of Biomechanics, vol. 41, pp , [7] J. Klauck, "Push-off forces vs kinematics in swimming turns: model based estimates of time-dependent variables.," Human Movements, vol. 6, pp , [8] N. Davey, D. James, A. Wixted, and Y. Ohgi, "A low cost self contained platform for human motion analysis," in The Impact of Technology on Sport II, F. K. Fuss, S. A., and U. S., Eds., ed London: Taylor & Francis, 2008, pp [9] A. J. Wixted, D. A. James, and D. V. Thiel, "Low Power Operating System and Wireless Networking for a Real Time Sensor Network," presented at the IEEE International Conference on IT and Applications, Bathurst, [10] S. Slawson, P. Conway, L. Justham, T. Le Sage, and A. West, "Dynamic signature for tumble turn performance in swimming," Procedia Engineering, vol. 2, pp , [11] T. Le Sage, P. Conway, L. Justham, S. Slawson, A. Bindel, and A. West, "A Component based Integrated System for Signal Processing of Swimming Performance," presented at the SIGMAP, Athen, [12] V. Gourgoulis, N. Aggeloussis, P. Kasimatis, N. Vezos, A. Boli, and G. Mavromatis, "Reconstruction accuracy in underwater three-dimensional kinematic analysis," Journal of Science and Medicine in Sport, vol. 11, pp , [13] A. B. J. Craig and D. R. Pendergast, "Relationships of stroke rate, distance per stroke, and velocity in competitive swimming," Medicine & Science in Sports & Exercise, vol. 11, pp , [14] Swift Performance Equipment. (2006, 24. August). Swift Sports Speed Probe 5000V [website accessed ]. Available: [15] A. B. J. Craig, B. Termin, and D. R. Pendergast, "Simultaneous recordings of velocity and video during swimming," Portugese Journal of Sport Sciences, vol. 6, pp , [16] M. Rejman and G. Borowska, "Searching for criteria in evaluating the monofin swimming turn from the perspective of coaching and improving technique," Journal of Sports Science and Medicine, vol. 7, p. 11, [17] P. J. Clothier, G. K. McElroy, B. A. Blanksby, and W. Payne, "A Comparison and Analysis of Traditional and Modified Freestyle Tumble Turns by Age-Group Swimmers," Journal of Human Movement Studies, vol. 38, pp , [18] D. James and N. Davey, "Swimming Stroke Analysis Using Multiple Accelerometer Devices and Tethered Systems," in The Impact of Technology on Sport II, 2007, pp [19] Y. Ohgi, H. Ichikawa, M. Homma, and C. Miyaji, "Stroke phase discrimination in breaststroke swimming using a triaxial acceleration sensor device," Sports Engineering, vol. 6, pp , 2003.