Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Epifanio, Irene; Ávila, Carolina; Page, Álvaro; Atienza, Carlos

doi:10.1007/s11517-008-0339-6

Cited by 30 publications

(26 citation statements)

References 28 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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“…The separated knee joint angle curves demonstrated similar patterns which, as expected, differed in their temporal characteristics. To maintain all the information of the curve shapes (magnitude and timing of local maxima and minima) the normative (representative) curve was created using two approaches: (a) averaging across the foot contact cycle without registration (unregistered curve), which is the most common approach in biomechanics [32], [33] and (b) performing a phase shift registration approach before averaging across the foot contact cycles as described by the following equations [13]:…”

Section: Technique Analysismentioning

confidence: 99%

Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors

Ahmadi

Mitchell

Destelle

et al. 2014

2014 11th International Conference on Wearable and Implantable Body Sensor Networks

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Abstract-Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in an outdoor training environment. We firstly present a system that automatically classifies a large range of training activities using the Discrete Wavelet Transform (DWT) in conjunction with a Random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subject's movement technique differed to the normative data in order to identify potential injury related factors. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments.

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“…Additionally, 21 most kinematic evaluations of the effects of footwear, sex, and age have focused on 22 biomechanical variables evaluated at discrete time points (Butler et al, 2006;Fukuchi 23 and Duarte, 2008;Hardin et al, 2004;Keenan et al, 2011 (Daffertshofer et al, 2004;Epifanio et al, 2008;Maurer et al, 2012; 31 Moore et al, 2009;Troje, 2002) and support vector machine (SVM) (Begg et al, 2005;32 Vapnik, 1995;Weston, 1999). The kinematic marker data from an entire stance phase 33 can then be used in one analysis step to determine lower-extremity differences for 34 certain groups.…”

mentioning

confidence: 99%

Shoe midsole hardness, sex and age effects on lower extremity kinematics during running

Nigg¹,

Baltich²,

Maurer³

et al. 2012

Journal of Biomechanics

119

103

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This file was dowloaded from the institutional repository Brage NIH -brage.bibsys.no/nih Nigg, B., Baltich, J., Maurer, C., Federolf, P. (2012) AbstractPrevious studies investigating the effects of shoe midsole hardness on running kinematics have often used male subjects from within a narrow age range. It is unknown whether shoe midsole hardness has the same kinematic effect on male and female runners as well as runners from different age categories. As sex and age have an effect on running kinematics, it is important to understand if shoe midsole hardness affects the kinematics of these groups in a similar fashion. However, current literature on the effects of sex and age on running kinematics are also limited to a narrow age range distribution in their study population. Therefore, this study tested the influence of three different midsole hardness conditions, sex and age on the lower extremity kinematics during heel-toe running. A comprehensive analysis approach was used to analyze the lower-extremity kinematic gait variables for 93 runners (male and female) aged 16-75 years. Participants ran at 3.33 ± 0.15 m/s on a 30 m-long runway with soft, medium and hard midsoles. A principal component analysis combined with a support vector machine showed that running kinematics based on shoe midsole hardness, sex, and age were separable and classifiable. Shoe midsole hardness demonstrated a subjectindependent effect on the kinematics of running. Additionally, it was found that age differences affected the more dominant movement components of running compared to differences due to the sex of a runner.

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Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Cited by 30 publications

References 28 publications

Archetypoid analysis for sports analytics

Archetypoid analysis for sports analytics

Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors

Shoe midsole hardness, sex and age effects on lower extremity kinematics during running

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