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      Technical Diagnosis on Elite Female Discus Athletes Based on Grey Relational Analysis

      research-article
      , ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          The personalized training of elite athletes is the key to the breakthrough of Chinese track and field events in the Tokyo Olympic Games. The brilliance of Chinese women's throwing benefits from the closed-loop personalized training system. Training starts from the construction of accurate personalized technology and physical fitness model. This paper introduces the concept of champion model and puts forward a targeted technical training system based on the differentiation research of champion model. This paper mainly studies and analyzes some important technical parameters and achievements of elite female discus athletes aged 2018∼2021 by using the methods of Pearson, partial correlation and grey correlation analysis. We select from several technical parameters with significant differences, choose from several technical parameters that have significant difference, and calculate the correlation parameters and results. The results show that the influence degree of these technical parameters is as follows: torso angle of right foot touching the ground, discus release angle, discus release speed, shoulder and arm passing angle of left foot off the ground, discus moving distance of double support, center of mass moving distance of double support, and time of the second single support stage. This is different from our view that the hand speed is the most important, so the training of elite athletes should be more refined and specialized to promote the improvement of their performance. Through the application of technical diagnosis model in Chen Yang in a period of time, Chen Yang got her best result (65.14 m) in Chongqing Athletics Championship, which verified the success of the technical diagnosis model and application in this study.

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          Most cited references29

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          Self-Supervised Deep Correlation Tracking

          The training of a feature extraction network typically requires abundant manually annotated training samples, making this a time-consuming and costly process. Accordingly, we propose an effective self-supervised learning-based tracker in a deep correlation framework (named: self-SDCT). Motivated by the forward-backward tracking consistency of a robust tracker, we propose a multi-cycle consistency loss as self-supervised information for learning feature extraction network from adjacent video frames. At the training stage, we generate pseudo-labels of consecutive video frames by forward-backward prediction under a Siamese correlation tracking framework and utilize the proposed multi-cycle consistency loss to learn a feature extraction network. Furthermore, we propose a similarity dropout strategy to enable some low-quality training sample pairs to be dropped and also adopt a cycle trajectory consistency loss in each sample pair to improve the training loss function. At the tracking stage, we employ the pre-trained feature extraction network to extract features and utilize a Siamese correlation tracking framework to locate the target using forward tracking alone. Extensive experimental results indicate that the proposed self-supervised deep correlation tracker (self-SDCT) achieves competitive tracking performance contrasted to state-of-the-art supervised and unsupervised tracking methods on standard evaluation benchmarks.
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            Robust visual tracking with correlation filters and metric learning

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              Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

              Background Robotic-Assisted Gait Training (RAGT) may enable high-intensive and task-specific gait training post-stroke. The effect of RAGT on gait movement patterns has however not been comprehensively reviewed. The purpose of this review was to summarize the evidence for potentially superior effects of RAGT on biomechanical measures of gait post-stroke when compared with non-robotic gait training alone. Methods Nine databases were searched using database-specific search terms from their inception until January 2021. We included randomized controlled trials investigating the effects of RAGT (e.g., using exoskeletons or end-effectors) on spatiotemporal, kinematic and kinetic parameters among adults suffering from any stage of stroke. Screening, data extraction and judgement of risk of bias (using the Cochrane Risk of bias 2 tool) were performed by 2–3 independent reviewers. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the certainty of evidence for the biomechanical gait measures of interest. Results Thirteen studies including a total of 412 individuals (mean age: 52–69 years; 264 males) met eligibility criteria and were included. RAGT was employed either as monotherapy or in combination with other therapies in a subacute or chronic phase post-stroke. The included studies showed a high risk of bias (n = 6), some concerns (n = 6) or a low risk of bias (n = 1). Meta-analyses using a random-effects model for gait speed, cadence, step length (non-affected side) and spatial asymmetry revealed no significant differences between the RAGT and comparator groups, while stride length (mean difference [MD] 2.86 cm), step length (affected side; MD 2.67 cm) and temporal asymmetry calculated in ratio-values (MD 0.09) improved slightly more in the RAGT groups. There were serious weaknesses with almost all GRADE domains (risk of bias, consistency, directness, or precision of the findings) for the included outcome measures (spatiotemporal and kinematic gait parameters). Kinetic parameters were not reported at all. Conclusion There were few relevant studies and the review synthesis revealed a very low certainty in current evidence for employing RAGT to improve gait biomechanics post-stroke. Further high-quality, robust clinical trials on RAGT that complement clinical data with biomechanical data are thus warranted to disentangle the potential effects of such interventions on gait biomechanics post-stroke. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-021-00857-9.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                15 April 2022
                : 2022
                : 8504369
                Affiliations
                China Athletics College, Beijing Sport University, Beijing 100085, China
                Author notes

                Academic Editor: Shahid Mumtaz

                Author information
                https://orcid.org/0000-0001-7338-7937
                https://orcid.org/0000-0001-8069-293X
                https://orcid.org/0000-0002-5797-0169
                Article
                10.1155/2022/8504369
                9033319
                35463289
                80023fd5-b1b8-4730-8c85-114d518feeda
                Copyright © 2022 Dong Chen 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
                : 14 January 2022
                : 21 March 2022
                : 24 March 2022
                Funding
                Funded by: Beijing Sport University
                Categories
                Research Article

                Neurosciences
                Neurosciences

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