Automatic detection of passing and shooting in water polo using machine learning: a feasibility study

Croteau, Félix; Thénault, François; Blain-Moraes, Stefanie; Pearsall, David J.; Paradelo, David; Robbins, Shawn M.

doi:10.1080/14763141.2022.2044507

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…These values may be used as features of importance in further research and used in further model refinement. Croteau et al (20) investigated the IMU detection of various subclasses of passing and shooting using support vector machine and artificial neural networks machine learning techniques using both two and one IMU sensors. They found that IMU could detect combined passing and shooting with 94% classification sensitivity when using support vector machine techniques and two IMU placed on the wrist and lower back.…”

Section: Discussionmentioning

confidence: 99%

“…They found that IMU could detect combined passing and shooting with 94% classification sensitivity when using support vector machine techniques and two IMU placed on the wrist and lower back. This study included both men and women to promote model robustness in throwing motion classification in a novel data set (20). Men's and women's water polo players shoot and swim with different velocities (21,22,34)-potentially complicating noise filtration parameters from an IMU signal in movements with lower magnitudes such as swimming.…”

Section: Discussionmentioning

confidence: 99%

“…They found that a number of machine learning algorithms achieved high accuracy and recommended future research into stroke volume metrics. In water polo, Croteau et al (20) investigated the feasibility of IMU and machine learning, particularly support vector machine and artificial neural networks, to classify throwing actions such as passing and shooting in a group consisting of both men and women. By combining passing and shooting detections of both men and women athletes, IMU were able to detect throwing with 94% sensitivity and 93% specificity.…”

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confidence: 99%

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A Dual Inertial Measurement Unit System for Classifying Standard Overhead Drill Movements in Elite Women’s Water Polo

King,

Lewis,

Watson

et al. 2023

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction Water polo upper limb external load monitoring cannot be currently measured accurately due to technological and methodological challenges. This is problematic as large fluctuations in overhead movement volume and intensity may affect performance and alter injury risk. Inertial measurement units (IMUs) and machine learning techniques have been shown to accurately classify overhead movements in other sports. We investigated the model accuracy and class precision, sensitivity and specificity of IMUs and machine learning techniques to classify standard overhead drill movements in elite women’s water polo. Methods Ten women’s water polo players performed standard drills of swimming, blocking, low and high intensity throwing under training conditions. Athletes wore two IMUs: one on the upper back and the other on the distal forearm. Each movement was videoed and coded to a standard overhead drill movement. IMU and coded video data were merged to verify the IMU detected activity classification of each movement to that of the video. Data were partitioned into a training and test set and used to form a decision tree algorithm. Model accuracy and class precision, sensitivity and specificity were assessed. Results IMU resultant acceleration and angular velocity values displayed drill specific values. 194 activities were identified by the model in the test set with 8 activities being incorrectly classified. Model accuracy was 95.88%. Percentage class precision, sensitivity and specificity were as follows: blocking (96.15, 86.21, 99.39), high intensity throwing (100, 100, 100), low intensity throwing (93.48, 93.48, 97.97) and swimming (94.81, 98.65, 96.67). Conclusions IMUs and machine learning techniques can accurately classify standard overhead drill movements in elite women’s water polo.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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A Dual Inertial Measurement Unit System for Classifying Standard Overhead Drill Movements in Elite Women’s Water Polo

King,

Lewis,

Watson

et al. 2023

Medicine &Amp; Science in Sports &Amp; Exercise

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“…Consequently, the authors conclude that further work is necessary to develop methods that can serve to also quantify training volume and intensity in water polo in terms of swimming and shooting. Preliminary work has shown the potential for wearable inertial measurement units (IMU) to accomplish this goal ( 7 , 17 ). Therefore, the purpose of this case report is to examine the outcomes of a monitoring strategy implemented for water polo goalkeepers using tools to document perceived exertion from training as well as estimates of training volume or intensity extracted from wearable IMU.…”

Section: Introductionmentioning

confidence: 99%

Case study of IMU loads and self-reported fatigue monitoring of water polo goalkeepers preparing for the Olympic games

Croteau

Gaudet²,

Briand³

et al. 2023

Front. Sports Act. Living

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IntroductionMeasurement of training in water polo goalkeepers has focused first on psycho-physiological variables, but also on external volume estimated with wearable sensors. However, there are limited studies exploring training monitoring in water polo goalkeepers longitudinally.MethodsThree female senior national team goalkeepers participated in this study from May to August 2021. Internal loads were defined using session rating of perceived exertion (sRPE). Tri-axial accelerations and angular velocities were measured with an inertial measurement unit (IMU) placed on the lower back to measure external loads. Relationships between self-reported and IMU-derived metrics were explored using Spearman correlations. Two-way ANOVAs were used to assess differences between session types and between athletes.ResultsIn total, 247 sessions were collected (159 practices, 67 matches and 21 game warm up), with 155 sessions having complete data. IMU metrics, such as number of kicks, number of jumps or player-load showed high correlation with each other (ρ = 0.80–0.88). There was also a moderate correlation (ρ = 0.47, 95% CI = 0.33–0.58) between sRPE and player-load measured with the IMU. ANOVA tests showed that there were significant differences between athletes for sRPE (p < 0.01) but not for player load (p = 0.47). There were no interactions between athletes and training types, except for index score (p < 0.01).ConclusionsThis study shows that monitoring of training loads can be performed successfully in water polo goalkeepers using a combination of self-reported and IMU measures. Self-reported outcomes can be expected to vary significantly across athletes within the same session, while IMU metrics vary across training situations. Finally, coaches should be mindful of missing data, as they can skew the interpretation of training loads.

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Automatic detection of skate strokes in short-track speed skating using one single IMU: validation of a new method

Clément,

Croteau,

Gagnon

et al. 2024

Sports Biomechanics

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Automatic detection of passing and shooting in water polo using machine learning: a feasibility study

Cited by 3 publications

References 29 publications

A Dual Inertial Measurement Unit System for Classifying Standard Overhead Drill Movements in Elite Women’s Water Polo

A Dual Inertial Measurement Unit System for Classifying Standard Overhead Drill Movements in Elite Women’s Water Polo

Case study of IMU loads and self-reported fatigue monitoring of water polo goalkeepers preparing for the Olympic games

Automatic detection of skate strokes in short-track speed skating using one single IMU: validation of a new method

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