With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.
Based on SSD to detect players, a super-pixel-based FCN-CNN player segmentation algorithm is proposed to filter out the complex background around players, which is more conducive to the subsequent pose estimation for target detection and fine localization of basketball technical features. The high resolution capability of CNN is used to extract images and perform computational preprocessing to identify typical basketball sports actions in video streams—rebounds, shots, and passes—with an accuracy rate of up to 95.6%. By comparing with three classical classification algorithms, the results prove that the target detection system proposed in this study is effective for target detection and fine localization of basketball sports technical features.
Introduction: Modern pentathlon has high requirements for the physical, psychological, and tactical training of athletes, and practicing the five items as a whole in physical training is a problem that needs to be solved. Organizing the load of each item and the overall load may be a circumventable problem using the altitude training technique. Objective: This study aimed to test and evaluate the effects of altitude training on modern pentathletes’ athletic performance and functional status. At the same time, we analyzed the method’s influence on the athletes’ physical quality. The ultimate goal of this experiment is to improve the science of modern pentathlete training. Methods: Six athletes from the modern pentathlon team were selected as research subjects. Changes in physiological indicators of the test subjects before and after altitude training were recorded. Mathematical statistics were used to analyze the collected data. Results: The athletes’ hemoglobin during high-altitude training was significantly higher than before training (P<0.05). Other physiological indicators such as blood urea and high-density protein were not significantly different (P>0.05). Modern pentathlon performance of athletes after altitude training was significantly improved (P<0.05). Conclusion: Altitude training can improve the performance of modern pentathlon athletes. At the same time, this training method can also improve the athletes’ aerobic capacity. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
There are limited studies examining the impacts of perfectionism and achievement motivation on collegiate athletes’ extra training and academic achievement in a Chinese context. This study aimed to examine the association of perfectionism (five facets) with extra training and academic performance among Chinese collegiate athletes and identify the mediating role of achievement motivation (two attributes) in the relationship between perfectionism and extra training and academic performance. With a prospective study design, 243 eligible participants completed two-wave surveys from September to December 2021. Measures included demographics, perfectionism (concern over mistake, CM; doubts about action, DA; personal standard, PS; organization; parental expectation, PE), achievement motivation (motive for success, MS; motive for avoiding failure, MF), extra-training (minutes/week), and academic performance (GPA). Results showed that CM, DA, PS, and MS were associated with extra training among Chinese collegiate athletes, while the associations of DA and PS with extra training were mediated by MS. In addition, DA, PS, organization, and MS were associated with participants’ GPA, while MS was a salient mediator for the contributions of DA and PS on participants GPA. Research findings give new insights to the psychological mechanisms of perfectionism and achievement motivation on collegiate athletes’ extra training and academic performance, contributing to future studies in relevant domains.
To increase the widespread attention of human gesture recognition technology, this paper proposes a basketball pose recognition method based on unit action division. Initially, the human gesture recognition algorithm is introduced for the verification of various effects and gestures of basketball players by monitoring various actions of basketball and to obtain the data of limbs using different detectors for different basketball movements. A large amount of data collection work was carried out for the experiment, and the corresponding experimental scenarios were described in the experimental design for testing. The methods for data processing and data division presented in this work are used to process the collected data. A feature vector set that describes a particular action is acquired and used as a sample set. The sample set is then delivered to the classifier. The classifier is implemented here based on the already-existing Weka platform, and performance evaluation and analysis of various classifiers are implemented. The results show that the differential limb function category has a better recognition effect on BP. The average accuracy of upper limb function was 92.19%, the average recall rate was 92.19%, and the accuracy of lower limb was much higher. The average accuracy of the four algorithms was within the range of 96.99% to 99.19% for lower limb movements and 84.89% to 92.19% for upper limb movements. The BP prosthetic network is used to create separate classifiers, ensuring that each basketball move was more than 95% accurate and that the average accuracy per basketball move was much more accurate. As a result, the accuracy level reached up to 98.85%. The validity of the basketball gesture recognition method recognized by the authors is sufficient and reasonable.
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