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Procedia Engineering 00 (2009) 000–000

Procedia Engineering

www.elsevier.com/locate/procedia

8th Conference of the International Sports Engineering Association (ISEA)

Wheelchair rugby: fast activity and performance analysis

Julian J. C. Chuaa, Franz Konstantin Fussa*, Vladimir V. Kulishb, Aleksandar Subica

aSchool of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne VIC 3083, Australia bSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

Received 31 January 2010; revised 7 March 2010; accepted 21 March 2010

Abstract

Post-match wheelchair rugby performance analysis is currently confined to single parameters (e.g. distance, average speed, activity time) without presenting the change of these parameters with time in the course of a match. The aim of this study is to determine a single activity parameter, which represents the instantaneous performance and which serves for comparing the performance between different players, as well as to develop a method which is relatively cheap and ubiquitously applicable. A rugby wheelchair was instrumented with an accelerometer (iPhone by Apple) and data were collected while pushing at all of the four combinations of high/low acceleration and high/low frequency. These four combinations were assigned performance ranks from 1 to 4, and the following activity parameters were determined: mean acceleration amplitude, mean stroke frequency, and the Hausdorff (fractal) dimension. The Hausdorff dimension correlated highly with the mean acceleration amplitude, mean frequency and performance rank as well as with the product of frequency and amplitude. The Hausdorff dimension of the signal is suitable to replace and represent conventional performance parameters.

© 2010 Published by Elsevier Ltd.

Keywords: wheelchair rugby; wheelchair; match analysis; performance; activity; fractal geometry; iPhone

1. Introduction

Wheelchair rugby is played in four periods of eight minutes with up to 12 players per wheelchair rugby team, and four out of 12 on the indoor court. Wheelchair rugby was developed for quadriplegic athletes as an alternative to wheelchair basketball. It is a combination of both rugby and basketball and the main aim of the game is to carry the ball across the opponent’s goal line, and likewise to prevent the other team from getting the ball or scoring. Like the game of rugby, wheelchair rugby requires the athletes to turn and accelerate quickly to avoid their opponents and score. Activity analysis (post-match or training) is essential for quantifying the performance of individual players and for applying it to player selection. Activity can be assessed semi-qualitatively (none, low, high activity), by using video techniques [1], or, more accurately, by measuring performance parameters, e.g. by means of accelerometers, gyrometers, magnetometers, GPS, velocimeters, etc. As wheelchair rugby is played indoors, activity assessment via GPS not possible. The performance parameters of wheelchair rugby are acceleration, handling and

* Corresponding author. Tel.: +61 3 9925 6123; fax: +61 3 9925 6108. E-mail address: franz.fuss@rmit.edu.au.

c© 2010 Published by Elsevier Ltd.

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1877-7058 c© 2010 Published by Elsevier Ltd.doi:10.1016/j.proeng.2010.04.114

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manoeuvrability of the wheelchair, turning speed, stroke frequency, velocity, power, input energy, etc. Sporner et al. [2] measured the number of wheel revolutions against time in a wheelchair tournament and calculated the means for distance travel, the average speed, the number of stops and starts, and the activity time. They did, however, not evaluate the change of these parameters with time in the course of a match. Simple activity analysis, e.g. in walking, running and swimming, is based on the movement frequency and/or the amplitude of acceleration. Quantifying the performance requires identifying individual strokes, locating the stroke peaks, counting the strokes, and measuring the amplitude and time between strokes [3]. This type of advanced performance analysis is only applicable to rhythmic activities, where the stroke frequencies do not significantly change over time. Cumulative activity analysis, however, based on mean accelerometer counts per minute, allows semi-qualitative assessment of non-rhythmic activities (sedentary, light, moderate, moderate-to-vigorous, and vigorous activity [4]; MVPA / moderate-to-vigorous physical activity [5, 6]).

Fractal geometry of signals was introduced for electric activity analysis of EEG signals [e.g. 7-11]. Fractal analysis of force-time data of sport climbing showed a strong correlation between the Hausdorff dimension and conventional performance parameters [12].

The aim of this study was to assess the activity pattern when riding a rugby wheelchair, by - analysing the stroke acceleration amplitude and stroke frequency, - determining the fractal (Hausdorff) dimension of the acceleration-time signal, - correlation the Hausdorff dimension data to the stroke acceleration amplitude and frequency, and - evaluating the suitability of the Hausdorff dimension as a stand-alone activity/performance parameter for post-

match analysis by displaying the fractal dimension with time.

2. Experimental

The experiments were carried out with a rugby wheelchair custom built by Melrose (Christchurch, New Zealand). The wheelchair was instrumented with an iPhone 3G (Apple Inc., Cupertino CA, USA), the acceleration data of which were collected at a frequency of 60 Hz. Two able-bodied test subjects performed the following tasks on a wooden gym floor: pushing at 1) high speed and acceleration at high frequency (“performance rank 1”; Figure 1), 2) high speed and acceleration at low frequency (“performance rank 2”), 3) low speed and acceleration at high frequency (“performance rank 3”), and 4) low speed and acceleration at low frequency (“performance rank 4”). The (integer) performance ranks were semi-quantitatively assigned according to the stroke impulse, which generally decreases from rank 1 to 4. 48 epochs, 5 seconds long (as suggested by [13]), of the data sets were analysed as to the following activity parameters: 1) mean acceleration amplitude of all strokes within each segment, 2) mean stroke frequency, and 3) the Hausdorff dimension. The latter was obtained from the software developed by Kulish et al. [7]. It was intended to replace the traditional activity parameters, signal amplitude and frequency, by a single parameter, the Hausdorff dimension in this case, for the ease of data comparability between different players. This Hausdorff dimension increases with the signal amplitude and frequency [12, 14]. Additionally, the Hausdorff dimension was taken from eight 5s-long data segments (“performance rank 5”; Figure 1) of low acceleration (coasting down without player activity) and zero acceleration (accelerometer noise at standstill). The Hausdorff dimension was correlated with the mean acceleration amplitude and frequency with a power regression, as the Hausdorff dimension is a power function of amplitude and frequency [12, 14]. Furthermore, the Hausdorff dimension was correlated to the performance rank (linear regression) and to the product of mean frequency and mean acceleration amplitude (power regression) in order to test whether the Hausdorff dimension is suitable as a stand-alone performance parameter.

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Figure 1: acceleration against time (5 second epochs); 1, 2, 3, 4, 5 = performance rank

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Figure 2: frequency against acceleration; 1, 2, 3, 4, 5 = performance rank Figure 3: Hausdorff dimension D0 against acceleration

Figure 4: Hausdorff dimension D0 against frequency Figure 5: Hausdorff dimension D0 against performance rank

3. Results

Figure 2 shows the frequency of the 5s-long data segments against the mean acceleration. The clusters of different performance ranks are clearly separated. The Hausdorff dimension correlated highly with the mean acceleration amplitude and frequency (Figures 3 and 4), except for the frequency data of performance rank 1 and 3, which shows a medium correlation. The Hausdorff dimension correlates highly with the semi-qualitative integer performance rank (Figure 5) as well as with the product of frequency and amplitude (Figure 6), which confirms that

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the fractal dimension is suitable to represent the overall performance and activity. Figure 7 shows the transition between two performance ranks by example, and exhibits the decrease of the Hausdorff dimension between performance ranks 1 and 3 when applying a sliding average fractal filter of a window width of 5 s, followed by a conventional sliding average filter (1st order Savitzky-Golay filter) of a window width of 1 s.

Figure 6: Hausdorff dimension D0 against product of mean frequency and mean amplitude Figure 7: Hausdorff dimension D0 against time, transition between performance rank 1 and 3 (sharp transition at 12 s; sliding average fractal filter of a window width of 5 s; solid horizontal lines: D0 per window; black dots: D0 in the middle of each window; grey bold curve: D0 after filtering with a running average of a 1 s window width; dashed lines: mean D0 of the two performance ranks before and after the transition).

4. Discussion

Analysis of fractal dimensions is a fast and accurate method [7] in contrast to the error prone stroke count and peak detection [3]. It is suitable to analyse an entire rugby wheelchair match lasting 32 minutes. The fractal analysis can be applied as a sliding average filter with a window width of 5 seconds (as suggested by [13]). Analysis of stroke amplitude and frequency is not recommended as the mean stroke frequency cannot be accurately determined with FFT (as seen in this study and subsequently abandoned), nor can the stroke peaks be accurately determined due to multiple sub-peaks per main peak. Filtering does not solve the problem either as the cut-off frequency depends on the actual stroke frequency. For example, a stroke frequency below 1 Hz has additional higher frequency components which match the main frequency of high-frequency activity (between 2 and 3 Hz).

The method suggested in this study, namely using an iPhone or iPod for data collection, is a cheap, yet accurate compared to more expensive accelerometers with higher sampling frequency and better resolution. Furthermore, iPhones are ubiquitous devices with multiple applications. The data sampling frequency of currently up to 100 Hz and the low resolution are not suitable for integration and accurately calculating the wheelchair speed. Running the iPhone at 60 Hz results in 115200 data per rugby match and accelerometer channel. This is an acceptable number of data for fast post-match fractal analysis. Integrating the fractal data with time provides an overall performance index per match, comparable to the data obtained by Sporner et al. [2].

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