Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

Yu, Gwangjae; Jang, Young Jae; Kim, Jihun; Kim, Jin Hae; Kim, Hye Young; Kim, Kitae; Panday, Siddhartha Bikram

doi:10.3390/s16040463

Cited by 69 publications

(78 citation statements)

References 31 publications

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“…During this phase, there is a point where the load is equal for both sides. It has been suggested that this point could correspond with the turn switch point (Müller and Schwameder, 2003;Yu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Turn Switch Time Points Measured by Portable Force Platforms and Pressure Insoles

Martínez

Nakazato

Scheiber

et al. 2020

Front. Sports Act. Living

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Several methods to determine turn switch points during alpine skiing using the vertical GRF exist in the literature. Although comparative studies between pressure insoles (PI) and force platforms (FP) have been conducted, there are no reports comparing the detected time points. Yet, these sensors and methods have been used interchangeably. This study aims to compare the turn switch time points with both sensors and various methods. Twenty skiers performed turns with FP and PI for two different ski styles (high and low dynamic turns). Three different assessment methodologies were compared: minima, functional minima, and crossings. Bland Altman and repeated measures ANOVA were used to assess statistical differences. Main effects of sensor and method were observed (p < 0.001). Although there was a low effect size (η 2 p = 0.013) between FP and PI, the 95% CI yielded values representing >30% of the turn duration. A large effect size (η 2 = 0.153) was found between the crossing method and the minima and functional minima methods. This indicates that those methods assess different events during the turn switch phase. In conclusion, the sensors and assessment methodologies compared in this study are not interchangeable with the possible exception of the minima and functional minima assessed with FP.

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Section: Introductionmentioning

confidence: 99%

Comparison of the Turn Switch Time Points Measured by Portable Force Platforms and Pressure Insoles

Martínez

Nakazato

Scheiber

et al. 2020

Front. Sports Act. Living

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“…Human Movement Science, 36, 107-122. https://doi.org/10.1016Science, 36, 107-122. https://doi.org/10. /j.humov.2014 1016 Yu, G., Jang, Y. J., Kim, J., Kim, J. H., Kim, H. Y., Kim, K., & Panday, S. B. (2016).…”

mentioning

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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“…The swing collection of golf wearable devices focused on motion sensors: accelerometers, gyroscopes, and magnetometers, 15 which are wellknown as inertial measurement units (IMUs). [10][11][12][13] In our case, strain gage sensors, three-axis accelerometer, and three-axis gyroscope are leveraged to collect the motion and trajectory of golf players and golf balls.…”

Section: Related Workmentioning

confidence: 99%

Golf swing classification with multiple deep convolutional neural networks

Jiao

Bie

et al. 2018

International Journal of Distributed Sensor Networks

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The use of smart sports equipment and body sensory systems supervising daily sports training is gradually emerging in professional and amateur sports; however, the problem of processing large amounts of data from sensors used in sport and discovering constructive knowledge is a novel topic and the focus of our research. In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. In particular, four convolutional neural classifiers are customized: ''GolfVanillaCNN'' with the convolutional layers, ''GolfVGG'' with the stacked convolutional layers, ''GolfInception'' with the multi-scale convolutional layers, and ''GolfResNet'' with the residual learning. Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network-based classifiers from two perspectives: classification implementations and sensor combinations. Besides, we further evaluate the performance of these four classifiers in terms of classification accuracy, precision-recall curves, and F1 scores. These common classification indicators illustrate that our convolutional neural network-based classifiers can basically group the golf swing predefined by the combination of shapes and golf players correctly and outperform support vector machine method representing traditional classification methods.

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Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

Cited by 69 publications

References 31 publications

Comparison of the Turn Switch Time Points Measured by Portable Force Platforms and Pressure Insoles

Comparison of the Turn Switch Time Points Measured by Portable Force Platforms and Pressure Insoles

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

Golf swing classification with multiple deep convolutional neural networks

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