Analyzing tennis game through sensor data with machine learning and multi-objective optimization

Mlakar, Miha; Luštrek, Mitja

doi:10.1145/3123024.3123163

Cited by 15 publications

(23 citation statements)

References 5 publications

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“…Thirteen studies reported or displayed classification matrices so the reader could calculate more than one evaluation metric. 6,7,13,15,[17][18][19][20][22][23][24][25] Depending on the nature of the classification problem, reporting only accuracy can lead to misrepresentation of the results; data with imbalanced classes (e.g. a low number of observations in one activity class relative to another) can lead to inflated overall accuracy.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The optimal placement location is likely specific to the type of sport being studied. 28 Five studies mounted an IMU solely on the participants' torso, 6,13,17,18,21 while the remaining 15 studies had at least one IMU located on an extremity (e.g. wrist or ankle).…”

Section: Imu Locationmentioning

confidence: 99%

“…Of the three sensors present in most IMUs, the magnetometer is used the least frequently because a controlled magnetic field without any distortion is required for optimal results, 31 especially with fast linear motions. 30 Two studies 3,20 used data from all three sensors, while the remaining studies used either a combination of the accelerometer and gyroscope 7,[11][12][13][14][15][16][17][18][19]21,22,25,32 or only the accelerometer. 4,6,23,24 Only one study investigated different combinations of sensors for classification of tennis strokes.…”

Section: Sensor Modalitymentioning

confidence: 99%

“…Fourteen studies specified the number of features used, ranging from 4 to~2000 features. Eight studies used only time-domain features, [10][11][12][13]15,19,24,25 while seven studies used a combination of features in the time and frequency domain. 4,7,16,17,20,22,23 No studies compared whether time or frequency domain features were more important for classifying certain movements; however, a broad range of features from both domains are likely to be beneficial.…”

Section: Pre-processingmentioning

confidence: 99%

See 3 more Smart Citations

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

McGrath

Neville

Cronin

2020

Proceedings of the Institution of Mechanical Engineers, Part P:

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Inertial measurement units (IMUs) are becoming increasingly popular in activity classification and workload measurement in sport. This systematic literature review focuses on upper body activity classification in court or field-based sports. The aim of this paper is to provide sport scientists and coaches with an overview of the past research in this area, as well as the processes and challenges involved in activity classification. The SPORTDiscus, PubMed and Scopus databases were searched, resulting in 20 articles. Both manually defined algorithms and machine learning approaches have been used to classify IMU data with varying degrees of success. Manually defined algorithms may offer simplicity and reduced computational demand, whereas machine learning may be beneficial for complex classification problems. Inter-study results show that no one machine learning model is best for activity classification; differences in sensor placement, IMU specification and pre-processing decisions can all affect model performance. Accurate classification of sporting activities could benefit players, coaches and team medical personnel by providing an objective estimate of workload. This could help to prevent injuries, enhance performance and provide valuable data to coaching staff.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Imu Locationmentioning

confidence: 99%

Section: Sensor Modalitymentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

See 2 more Smart Citations

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

McGrath

Neville

Cronin

2020

Proceedings of the Institution of Mechanical Engineers, Part P:

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“…Even today the analysis of sport videos is mostly done by human sports experts [1] which is expensive and time consuming. Other techniques rely on special camera setup [28] or additional sensors [20] which adds to the cost as well as limits their utility. Deep learning based techniques have enabled a significant rise in the performance of various tasks such as object detection and recognition [11,26,27,32], action recognition [40], and temporal segmentation [18,33].…”

Section: Introductionmentioning

confidence: 99%

Towards Structured Analysis of Broadcast Badminton Videos

Ghosh

Singh

Jawahar

2018

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors.Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.

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Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

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The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

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Analyzing tennis game through sensor data with machine learning and multi-objective optimization

Cited by 15 publications

References 5 publications

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

Upper body activity classification using an inertial measurement unit in court and field-based sports: A systematic review

Towards Structured Analysis of Broadcast Badminton Videos

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

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