Analysis of wheelchair rugby accelerations with fractal dimensions

Fuss, Franz Konstantin; Subic, Aleksandar; Chua, Julian J.C.

doi:10.1016/j.proeng.2012.04.075

Cited by 10 publications

(11 citation statements)

References 5 publications

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“…Horizontal frame rotation estimates were used to correct the wheel gyroscope signal for wheel camber angle, as described by Pansiot et al (2011), Fuss et al (2012) and van der Slikke et al…”

Section: Figurementioning

confidence: 99%

“…However, this may result in an abundance of sometimes hard to interpret kinematic data. Usma et al (2010) used IMUs to determine performance of wheelchair rugby players in a standard agility test while Fuss et al (2012) used fractal dimension analysis of frame acceleration to identify activity patterns during wheelchair rugby match play. A newly developed method utilizing IMUs (van der Slikke et al, 2015a) appeared reliable for measuring an extensive set of wheelchair kinematic outcomes, but was not yet applied in actual match play and lacked usability for sports practice given the bulk of outcomes provided.…”

Section: Introductionmentioning

confidence: 99%

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From big data to rich data: The key features of athlete wheelchair mobility performance

Slikke

Berger

Bregman

et al. 2016

Journal of Biomechanics

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27Quantitative assessment of an athlete's individual wheelchair mobility performance is one prerequisite 28 needed to evaluate game performance, improve wheelchair settings and optimize training routines. 29Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative 30 assessment, providing a large number of kinematic data. The goal of this research was to reduce that 31 large amount of data to a set of key features best describing wheelchair mobility performance in 32 match play and present them in meaningful way for both scientists and athletes. To test the 33 discriminative power, wheelchair mobility characteristics of athletes with different performance levels 34 were compared. 35The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using 36 three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 37 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and 38 acceleration; average and best performance. In addition, it was explored whether groups of athletes 39 with known performance differences based on their impairment classification also differed with 40 respect to these key outcomes using univariate general linear models. For all six key outcomes 41 classification showed to be a significant factor (p<0.05). 42We composed a set of six key kinematic outcomes that accurately describe wheelchair mobility 43 performance in match play. The key kinematic outcomes were displayed in an easy to interpret way, 44 usable for athletes, coaches and scientist. This standardized representation enables comparison of 45 different wheelchair sports regarding wheelchair mobility, but also evaluation at the level of an 46 individual athlete. By this means, the tool could enhance further development of wheelchair sports in 47 general. 48

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From big data to rich data: The key features of athlete wheelchair mobility performance

Slikke

Berger

Bregman

et al. 2016

Journal of Biomechanics

View full text Add to dashboard Cite

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“…Acceleration of a wheelchair recorded at 60 Hz during a wheelchair rugby match (data from [15]) against time (bottom subfigure); fractal dimension D H (running average with a window width of 151 data points over 2.5 s) of the acceleration signal calculated with three different methods (middle subfigure): Higuchi's [7] method, Raghavendra and Dutt's [9] MRBC, and the method developed in this study (cf. Section 4); top subfigure: absolute difference in D H between the three methods.…”

Section: Figurementioning

confidence: 99%

“…Acceleration of a wheelchair recorded at 60 Hz during a wheelchair rugby match (data from [15]) against time (c); the two arrows refer to the time data with the lowest and highest D H used for determining the amplitude spectrum of Figure 11; (b) fractal dimension D Hm (running average with a window width of 151 data points over 2.5 s) of the acceleration signal calculated for different amplitude multipliers m (cf. Figure 11); note that the fractal dimension at m → ∞ corresponds to the one calculated with Higuchi's method in Figure 2; (a) optimised D Hm at m = 0.8.…”

Section: Figurementioning

confidence: 99%

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

Fuss

2013

Computational and Mathematical Methods in Medicine

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Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

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“…The methods and results of this paper validate the new algorithm against known baselines to evidence the accuracy and validity of the measurements. This algorithm can be linked with other algorithms-for example, Fuss et al's [7] fractional dimensioning approach to classify in-game activities or Bergamini et al's [3] measurement of wrist synchronicity-to provide coaches with enhanced information, pertinent understanding, and improved performance, mitigating against injury and improving wheelchair design.…”

Section: Introductionmentioning

confidence: 99%

A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

et al. 2016

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Abstract:With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations.

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Analysis of wheelchair rugby accelerations with fractal dimensions

Cited by 10 publications

References 5 publications

From big data to rich data: The key features of athlete wheelchair mobility performance

From big data to rich data: The key features of athlete wheelchair mobility performance

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

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