2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

      research-article
      1 , , 2 , 1
      Computational Intelligence and Neuroscience
      Hindawi

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper constructs a sports action recognition model based on deep learning (DL) and clustering extraction algorithm. For the input detection image frame, athletes' movements are detected through DL network, and then athletes' sports movements are fused. Moreover, it expands new knowledge and improves learning ability through automatic learning training set. The neural network (NN) is applied to the sample set containing images of nonathletes, and the negative training sample set is iteratively enhanced according to the generated false positives, and the results are optimized by clustering method. Simulation experiments show that compared with other algorithms, the clustering extraction algorithm in this paper has achieved superior performance in recognition rate and false alarm rate, and the recognition speed is faster. The aim is to extract the athletes' training postures through the analysis of sports movements, so as to assist coaches to train athletes more professionally and provide some reference for sports movement recognition.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: not found
          • Article: not found

          Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Action Recognition From Depth Maps Using Deep Convolutional Neural Networks

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

              Background Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. Methods We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. Results A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. Conclusions The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.
                Bookmark

                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                19 March 2022
                : 2022
                : 4887470
                Affiliations
                1School of Science, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163316, China
                2School of Foreign Languages, Northeast Petroleum University, Daqing, Heilongjiang 163000, China
                Author notes

                Academic Editor: Rahim Khan

                Author information
                https://orcid.org/0000-0001-8463-8252
                Article
                10.1155/2022/4887470
                8957414
                265f006e-9ddc-4b98-a15c-62138d7c0936
                Copyright © 2022 Ming Fu et al.

                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
                : 12 January 2022
                : 10 February 2022
                : 1 March 2022
                Categories
                Research Article

                Neurosciences
                Neurosciences

                Comments

                Comment on this article