A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

Rajšp, Alen; Fister, Iztok

doi:10.3390/app10093013

Cited by 89 publications

(55 citation statements)

References 143 publications

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“…In this work, an IMU embedded racket was used as the data collection tool, which increased the privacy (not visual) and anonymity of players' issues, thus increasing the players' confidence. Moreover, the practical and close cooperation among all the study's parties (the players, trainers, and researchers) in the design and data collection phases of the research could overcome the challenge mentioned in the recent Rajšp and Fister's study [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, to evaluate each type of Forehand strokes (Basic, Topspin, and Push), three separate ML models are needed. To benchmark the study, a conventional regression method (SVR) and two commonly used deep neural networks (LSTM and CNN) were utilized [ 41 ]. We took a sequence of the features x 1 ,…, x 840 as input data for the ML algorithms representing the sensory-data dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Literature review revealed that, in the case of conventional regression models, SVR is mostly applied. In the deep learning techniques, the LSTM and CNN are most commonly used to estimate time-series sensory data [ 41 ]. Besides, it has revealed that, in the literature, stroke quality evaluation of Table Tennis Forehand based on a single object sensor has yet to be considered.…”

Section: Related Studiesmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

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Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

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A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

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“…Because there are some uncertain factors in the competition scene, it will affect the recognition of the visual target. According to the distribution of white light in the scene and the lighting angle and the difference of various places, some places will reflect the intensity of light, and the objects taken by the camera are very bright, forming bright areas [23]. The other part of the field of view reflects weak light, and the object captured by the camera looks very dark, forming a dark area, which makes it difficult to ensure that the same color code displays the same color in different positions of the image [24].…”

Section: A Factors Affecting the Visual Subsystem And The Choice Of mentioning

confidence: 99%

Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

et al. 2020

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The purpose of this article is to identify football and related environmental variables through its VR images under the current situation where the vision system has become the only way for football robots to obtain the external environment, so as to improve the chances of winning the game. First of all, this article uses color filtering to enhance the football characteristics in the VR perspective, so as to distinguish the football from the off-field environment and shadows. The optimal threshold of the enhanced image is automatically determined by the Ostu method, so the image will not be affected by shadows during segmentation, and the contour of the image can be segmented. At the same time, using the humanoid medium-sized football game machine system as the platform, the relevant processing algorithms of the humanoid football robot front-view system are studied to realize the work of color image segmentation, edge extraction, straight line extraction, cross-line recognition and target post-recognition. PA-SIFT algorithm is used to quickly identify the graphics. Data verification results show that the recognition rate of the PA-SIFT algorithm can reach 96%, ensuring the real-time and feasibility of the algorithm. In addition, the divide-and-conquer algorithm and the related processing algorithm of the vision system are combined to determine the central area of the image, so that the algorithm is not affected by the external environment, and the algorithm is robust and can improve actual competition.

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“…Although hockey games are fascinating to watch, the use of analytical approaches to assess player performance is still at an early age due to the games' low scores [2] and complex dynamics [3,4]. Evaluating the performance of individual players and their contribution to the overall performance of the team [5,6] is a major challenge in the field of sports analysis. Several metrics have been proposed for performance analysis in different team sports, e.g., "Expected-Point-Value" in basketball [7,8] and "Expected-Goal-Value" in soccer [9] and American football [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model

Guo

Tao

et al. 2020

IEEE Access

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The accurate detection of ice hockey players and teams during a game is crucial to the tracking of individual players on the rink and team tactical decision making and is therefore becoming an important task for coaches and other analysts. However, hockey is a fluid sport due to its complex situation and the frequent substitutions by both teams, resulting in the players taking various postures during a game. Few player detection models from basketball and soccer take these characteristics into account, especially for team detection without prior annotations. Here, a two-phase cascaded convolutional neural network (CNN) model is designed for the detection of individual ice hockey players, and the jersey color of the detected players is extracted to further identify team affiliations. Our model filters most of the disturbing information, such as the audience and sideline advertising bars, in Phase I and refines the detection of the targeted players in Phase II, resulting in an accurate detection with a precision of 98.75% and a recall of 94.11% for individual players and an average accuracy of 93.05% for team classification with a self-built dataset of collected images from the 2018 Winter Olympics. The results for the regular season games of the 2019-2020 National Hockey League (NHL) covering all 31 teams are also presented to show the robustness of our model. Compared to state-of-the-art approaches, our player detection model achieves the highest accuracy with the self-built dataset.

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A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

Cited by 89 publications

References 143 publications

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model

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