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      Retracted: Footballer Action Tracking and Intervention Using Deep Learning Algorithm

      retraction
      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. In addition, our investigation has also shown that one or more of the following human-subject reporting requirements has not been met in this article: ethical approval by an Institutional Review Board (IRB) committee or equivalent, patient/participant consent to participate, and/or agreement to publish patient/participant details (where relevant). Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          • Abstract: found
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          Footballer Action Tracking and Intervention Using Deep Learning Algorithm

          Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach's eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer's motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer's posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.
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            Author and article information

            Contributors
            Journal
            J Healthc Eng
            J Healthc Eng
            JHE
            Journal of Healthcare Engineering
            Hindawi
            2040-2295
            2040-2309
            2023
            11 October 2023
            11 October 2023
            : 2023
            : 9768638
            Affiliations
            Article
            10.1155/2023/9768638
            10584584
            37860328
            c03a184b-bc17-4bd4-a655-98b8c7f261bb
            Copyright © 2023 Journal of Healthcare Engineering.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 10 October 2023
            : 10 October 2023
            Categories
            Retraction

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