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Kong Y, Duan Z. Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant. Sci Rep 2024; 14:7640. [PMID: 38561402 PMCID: PMC10984940 DOI: 10.1038/s41598-024-58518-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 03/30/2024] [Indexed: 04/04/2024] Open
Abstract
The purpose of this study is to deeply understand the psychological state of boxers before the competition, and explore an efficient boxing action classification and recognition model supported by artificial intelligence (AI) technology through these psychological characteristics. Firstly, this study systematically measures the key psychological dimensions of boxers, such as anxiety level, self-confidence, team identity, and opponent attitude, through psychological scale survey to obtain detailed psychological data. Then, based on these data, this study innovatively constructs a boxing action classification and recognition model based on BERT fusion 3D-ResNet, which not only comprehensively considers psychological information, but also carefully considers action characteristics to improve the classification accuracy of boxing actions. The performance evaluation shows that the model proposed in this study is significantly superior to the traditional model in terms of loss value, accuracy and F1 value, and the accuracy reaches 96.86%. Therefore, through the comprehensive application of psychology and deep learning, this study successfully constructs a boxing action classification and recognition model that can fully understand the psychological state of boxers, which provides strong support for the psychological training and action classification of boxers.
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Affiliation(s)
- Yuanhui Kong
- School of Science of Physical Culture and Sports, Kunsan University, Kunsan, 54150, Korea
| | - Zhiyuan Duan
- School of Science of Physical Culture and Sports, Kunsan University, Kunsan, 54150, Korea.
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Han H, Yang C, Jiang B, Shang C, Sun Y, Zhao X, Xiang D, Zhang H, Shi Y. Construction of chub mackerel (Scomber japonicus) fishing ground prediction model in the northwestern Pacific Ocean based on deep learning and marine environmental variables. MARINE POLLUTION BULLETIN 2023; 193:115158. [PMID: 37321004 DOI: 10.1016/j.marpolbul.2023.115158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/18/2023] [Accepted: 06/06/2023] [Indexed: 06/17/2023]
Abstract
Accurate prediction of the central fishing grounds of chub mackerel is substantial for assessing and managing marine fishery resources. Based on the high-seas chub mackerel fishery statistics and multi-factor ocean remote-sensing environmental data in the Northwest Pacific Ocean from 2014 to 2021, this article applied the gravity center of the fishing grounds, 2DCNN, and 3DCNN models to analyze the spatial and temporal variability of the chub mackerel catches and fishing grounds. Results:1) the primary fishing season of chub mackerel fishery was April-November which catches were mainly concentrated in 39°∼43°N, 149°∼154°E. 2) Since 2019, the annual gravity center of the fishing grounds has continued to move northeastward; the monthly gravity center has prominent seasonal migratory characteristics. 3) 3DCNN model was better than the 2DCNN model. 4) For 3DCNN, the model prioritized learning information on the most easily distinguishable ocean remote-sensing environmental variables in different classifications.
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Affiliation(s)
- Haibin Han
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Chao Yang
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Bohui Jiang
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Chen Shang
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Yuyan Sun
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Xinye Zhao
- College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Delong Xiang
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China; College of Marine Sciences, Shanghai Ocean University, Shanghai, China
| | - Heng Zhang
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China.
| | - Yongchuang Shi
- Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, 200090, China.
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Surek GAS, Seman LO, Stefenon SF, Mariani VC, Coelho LDS. Video-Based Human Activity Recognition Using Deep Learning Approaches. SENSORS (BASEL, SWITZERLAND) 2023; 23:6384. [PMID: 37514677 PMCID: PMC10386633 DOI: 10.3390/s23146384] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023]
Abstract
Due to its capacity to gather vast, high-level data about human activity from wearable or stationary sensors, human activity recognition substantially impacts people's day-to-day lives. Multiple people and things may be seen acting in the video, dispersed throughout the frame in various places. Because of this, modeling the interactions between many entities in spatial dimensions is necessary for visual reasoning in the action recognition task. The main aim of this paper is to evaluate and map the current scenario of human actions in red, green, and blue videos, based on deep learning models. A residual network (ResNet) and a vision transformer architecture (ViT) with a semi-supervised learning approach are evaluated. The DINO (self-DIstillation with NO labels) is used to enhance the potential of the ResNet and ViT. The evaluated benchmark is the human motion database (HMDB51), which tries to better capture the richness and complexity of human actions. The obtained results for video classification with the proposed ViT are promising based on performance metrics and results from the recent literature. The results obtained using a bi-dimensional ViT with long short-term memory demonstrated great performance in human action recognition when applied to the HMDB51 dataset. The mentioned architecture presented 96.7 ± 0.35% and 41.0 ± 0.27% in terms of accuracy (mean ± standard deviation values) in the train and test phases of the HMDB51 dataset, respectively.
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Affiliation(s)
- Guilherme Augusto Silva Surek
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil
| | - Laio Oriel Seman
- Graduate Program in Applied Computer Science, University of Vale do Itajai, Itajai 88302-901, Brazil
| | - Stefano Frizzo Stefenon
- Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Viviana Cocco Mariani
- Department of Electrical Engineering, Federal University of Parana (UFPR), Curitiba 81530-000, Brazil
- Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil
| | - Leandro Dos Santos Coelho
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil
- Department of Electrical Engineering, Federal University of Parana (UFPR), Curitiba 81530-000, Brazil
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Traffic Congestion Classification Using GAN-Based Synthetic Data Augmentation and a Novel 5-Layer Convolutional Neural Network Model. ELECTRONICS 2022. [DOI: 10.3390/electronics11152290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).
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