Judging complex movement performances for excellence: A principal components analysis-based technique applied to competitive diving

Young, Cole R.; Reinkensmeyer, David J.

doi:10.1016/j.humov.2014.05.009

Cited by 21 publications

(18 citation statements)

References 11 publications

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“…While efficiency has received little attention in motor skill research, mainly due to the limited relevance of energy expenditure in many complex tasks, it is of great interest in endurance sports . Endurance performance is closely linked to the economy of locomotion which, in turn, is related to effective movement mechanics . Our results are in line with a previous study that showed that elite cross‐country skiers have a 4%‐15% better skiing economy than recreational skiers .…”

Section: Discussionsupporting

confidence: 90%

“…The PCA method can reveal the components with the largest kinematic variance, making it particularly suited to show the “synergies” or the “coordinative structures” by which the motor system organizes movement . Movements represented in the same component have, by definition, the same frequency and phase, and are therefore synergic movements.…”

Section: Discussionmentioning

confidence: 99%

“…To date, PCA has received less attention than it deserves in the biomechanical analysis of sports techniques. Its application was recently demonstrated as being helpful in the analysis of alpine ski turns, cycling, diving, karate, and juggling …”

Section: Introductionmentioning

confidence: 99%

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Cross‐country skiing movement factorization to explore relationships between skiing economy and athletes' skills

Pellegrini

Zoppirolli

Boccia

et al. 2017

Scandinavian Med Sci Sports

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We investigated the relationships between the biomechanics of the double poling (DP) technique in cross-country skiing, its economy, and athletes' skill. To this aim, skiers' motion has been factorized into components through principal component analysis (PCA). Eight high-level (HL) and eight regional level (RL) male cross-country skiers performed a 5-minute submaximal DP trial while roller skiing on a treadmill at 14 km h and 2° incline. Whole-body kinematics was recorded with a motion capture system. PCA was applied to markers coordinates to extract principal movements (PM ), which were ranked by their variance. Energy cost (EC) of locomotion was calculated from ergospirometric measurements. Results showed that 96.7%±0.6% of total skiing pattern variance can be described with the first three PM (Shoulder and trunk flexion-extension are described PM and PM and elbow flexion-extension are mainly represented in PM and PM The variance of further components, consisting of residual movements (eg, slow postural changes or high-frequency vibrations), was greater for the RL than the HL skiers (4.0%±0.5% vs 2.6%±0.3%; P<.001) and was positively correlated with EC (R =.646; P<.001). PCA permitted to describe the biomechanics of the DP technique through a limited set of principal movements. Skiing skills and economy appeared to be related to a skier's ability to simplify movement complexity, suggesting that an efficient skier is better able to reduce superfluous movement components during DP.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

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Cross‐country skiing movement factorization to explore relationships between skiing economy and athletes' skills

Pellegrini

Zoppirolli

Boccia

et al. 2017

Scandinavian Med Sci Sports

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“…It is therefore particularly well suited for analysing the correlated patterns of segment movements that constitute "technique" in sports (Witte, Ganter, Baumgart, & Peham, 2010). PCA has already been applied for technique analysis in dance (Bronner & Shippen, 2015), diving (Young & Reinkensmeyer, 2014), gymnastics (G. K. Williams et al, 2016), juggling (Huys, Daffertshofer, & Beek, 2004), walking and running (Dona, Preatoni, Cobelli, Rodano, & Harrison, 2009;Lamoth, Daffertshofer, Huys, & Beek, 2009), skiing (Federolf et al, 2014), soccer (Diaz, Fajen, & Phillips, 2012) and tennis (Huys, Smeeton, Hodges, Beek, & Wiliams, 2008;Smeeton & Huys, 2011). However, an important limitation in many of the previous analyses is that separate PCAs were calculated for each individual participant.…”

Section: Introductionmentioning

confidence: 99%

Technique analysis in elite athletes using principal component analysis

Gløersen

Myklebust

Hallén

et al. 2017

Journal of Sports Sciences

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The aim of this study was to advance current movement analysis methodology to enable a technique analysis in sports facilitating (1) concurrent comparison of the techniques between several athletes; (2) identification of potentially beneficial technique modifications and (3) a visual representation of the findings for feedback to the athletes. Six elite cross-country skiers, three world cup winners and three national elite, roller ski skated using the V2 technique on a treadmill while their movement patterns were recorded using 41 reflective markers. A principal component analysis performed on the marker positions resulted in multi-segmental "principal" movement components (PMs). A novel normalisation facilitated comparability of the PMs between athletes. Additionally, centre of mass (COM) trajectories were modelled. We found correlations between the athletes' performance levels (judged from race points) and specific features in the PMs and in the COM trajectories. Plausible links between COM trajectories and PMs were observed, suggesting that better performing skiers exhibited a different, possibly more efficient use of their body mass for propulsion. The analysis presented in the current study revealed specific technique features that appeared to relate to the skiers' performance levels. How changing these features would affect an individual athlete's technique was visualised with animated stick figures.

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“…These identified variables 143 are represented in a compact feature variable (Mannini & Sabatini, 2010). (Gløersen, Myklebust, Hallén, & Federolf, 2018;Young & 145 Reinkensmeyer, 2014), vector coding techniques (Hafer & Boyer, 2017) and empirical cumulative 146 distribution functions (ECDF) (Plötz, Hammerla, & Olivier, 2011). An ECDF approach has been 147 shown to be advantageous over PCA as it derives representations of raw input independent of the and analysis of IMU data for sports application and vision-based human activity recognition, see and (Bux et al, 2017), respectively.…”

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confidence: 99%

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

219

148

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Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data preprocessing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental setup , data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).

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Judging complex movement performances for excellence: A principal components analysis-based technique applied to competitive diving

Cited by 21 publications

References 11 publications

Cross‐country skiing movement factorization to explore relationships between skiing economy and athletes' skills

Cross‐country skiing movement factorization to explore relationships between skiing economy and athletes' skills

Technique analysis in elite athletes using principal component analysis

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

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