A novel approach to identify and quantify activity and performance in wheelchair rugby

Existing methods for performance and activity monitoring of court-based wheelchair sports such as wheelchair rugby during actual matches have their limitations. They either require too much manual efforts or they gather insufficient information. Inertia sensors have the ability to measure substantial amounts of movement data but there is no known method to decipher that huge amount of data for quantifying activity or performance. Based on literature, Fractal dimensions have been applied to signals of physical parameters measured as a time series in the field of sports, biomedical and manufacturing. In all these cases Fractal dimensions of the time-based signals were able to identify different states or conditions accurately. There are several methods of determining Fractal dimensions and for this study, two were narrowed down – one based on Renyi’s generalized dimension (S0) and the other based on Hausdorff dimension (DH). A feasibility study was first conducted to investigate the Fractal dimensions of forward accelerations during manual wheelchair pushing using the two methods. The outcome showed that generally higher Fractal dimension values were linked to higher push amplitudes and frequencies or a higher activeness level. It was identified that S0 related to energy released to the environment while DH showed a connection with activity level. This was then taken further by capturing forward/backward accelerations of wheelchairs during actual wheelchair rugby matches. S0 and DH were calculated from the acceleration data, and four methods were developed using S0 and DH values to identify and quantify activity and performance of the wheelchair rugby athletes. Those methods include cumulative plots of S0 and DH; a Decision template formed using a 2D plot of S0 against DH, and Activities Ranking that is also based on the 2D plot. After the basic process of the methods was developed, steps were taken to optimize the values of S0 and DH such that they are optimal for the identification and quantification outcome of wheelchair rugby activities. The factors that influence S0 and DH values include parameters of the inertia sensing device (sensor resolution and sampling rate), running average window width and amplitude multiplier for calculating DH. In the end, although the number of athletes that were tested was small, the analysis outcome supported results from previous studies where athletes of higher functional classifications showed higher performance. The analysis of activity ranking which had an accuracy of 95% also highlighted that difference in activities between the athletes related highly with their functional classifications and their role or position in the team. The results of the analysis proved to be relevant for coaching, planning matches and even for talent identification