Functional Data Analysis of Running Kinematics in Chronic Achilles Tendon Injury

Donoghue, Orna A.; Harrison, Andrew J.; Coffey, Norma; Hayes, Kevin

doi:10.1249/mss.0b013e31816c4807

Cited by 106 publications

(99 citation statements)

References 26 publications

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“…In spite of the fact that time series data or movement trajectories are common in sports, we have only found applications in sport biomechanics or medicine (Epifanio et al, 2008;Harrison et al, 2007;Donoghue et al, 2008;Harrison, 2014) and player's ageing curves (Wakim and Jin, 2014). In Wakim and Jin (2014), k-means clustering of PCA scores computed as proposed by Yao and Müller (2005) is performed for Win Shares on a different database from those we use.…”

Section: Related Workmentioning

confidence: 99%

Archetypoid analysis for sports analytics

Vinué

Epifanio

2017

Data Min Knowl Disc

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We intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete's careers.

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Section: Related Workmentioning

confidence: 99%

Archetypoid analysis for sports analytics

Vinué

Epifanio

2017

Data Min Knowl Disc

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“…In consequence, biomechanists have sought new ways to analyse data as a continuous signal, [1][2][3] such as functional data analysis which: examines a sample of curves described by functions rather than discrete data points, does not require linear time normalization which can alter the data, 4 and uncovers the underlying structure while maintaining all of the signal information. [4][5][6][7][8][9][10] However, there are two possible limitations to functional data analysis as currently employed in biomechanics. [4][5][6][7][8][9][10] Firstly, it does not inherently identify key-phases, a it tends to be applied to the whole function assuming that key-phases have an overwhelming effect on the generated output score.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7][8][9][10] However, there are two possible limitations to functional data analysis as currently employed in biomechanics. [4][5][6][7][8][9][10] Firstly, it does not inherently identify key-phases, a it tends to be applied to the whole function assuming that key-phases have an overwhelming effect on the generated output score. In consequence, it has the potential to mask performance related factors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Characterizing Phases on Waveforms: An Application to Vertical Jumps

Richter

O’Connor²,

Marshall³

et al. 2014

Journal of Applied Biomechanics

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Abstract:The aim of this study is to propose a novel data analysis approach, 'Analysis of Characterizing Phases' (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude and magnitude-time domain; and to compare the findings of ACP to discrete point analysis in identifying performance related factors in vertical jumps. Twenty five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (p = .006) and the time from initial-tomaximum force (p = .047) as performance related factors. However, due to inter-subject variability in the shape of the force curves (i.e non-, uni-and bi-modal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to:apply forces for longer (p < .038), generate higher forces (p < .027) and produce a greater rate of force development (p < .003) as performance related factors. Analysis of Characterizing Phases showed advantages over discrete point analysis in identifying performance related factors because it: (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains.

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“…In these applications, one PCA is often conducted for each joint/muscle/segment using a subjects × time matrix, where the subjects mode is a collection of subjects from different groups (e.g., Astephen et al, 2008;Chester & Wrigley, 2008;Cochran et al, 1984;Donoghue et al, 2008;Lamoth et al, 2006;Lauer et al, 2005;Lee et al, 2009;McKean et al, 2007). The PCs from these separate analyses are then examined to understand gait patterns of different subject groups, and PC scores (see Section 2.1.1) are often used to quantify between-group differences.…”

Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three-or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lowerlimb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).ii To my loving and supportive family.iii

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Functional Data Analysis of Running Kinematics in Chronic Achilles Tendon Injury

Cited by 106 publications

References 26 publications

Archetypoid analysis for sports analytics

Archetypoid analysis for sports analytics

Analysis of Characterizing Phases on Waveforms: An Application to Vertical Jumps

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

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