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The application of information technology has realized the transformation of production methods and people's lifestyles and supported sports development, which is becoming more and more powerful in sports. Because of technological developments, personal endurance athletes, sports teams, and physicians can now track lifting tasks, the volume of work, and biomarkers to maximize efficiency and minimize injuries. Therefore, this paper used its advanced methods and information display technology to optimize aerobics in the presence of artificial intelligence and promote the ecological development of sports. In addition, this research work also took the elective course of aerobics major of Liaoning Normal University in 2019 as an example and adopted bio-pulse sensors as a new teaching method. Besides, this paper used questionnaires, teaching experiments, etc., combined with other course teaching practices and aerobics-related teaching experiments, and evaluated the teaching effect according to the measurement indicators. The experimental results have shown a significant difference in the students' learning attitude in the experimental group and the control group using the bio-pulse sensor. The P values, i.e., the probability values for obtaining the results, were 0.047 and 0.048, all of which were less than 0.05, so they were considered statistically significant. Using bio-pulse sensors to select aerobics courses will give full play to the richness of teaching content and novel teaching methods, which will help stimulate students' interest in learning, stimulate students' enthusiasm for learning, and improve teaching quality. [ABSTRACT FROM AUTHOR]

In the process of calisthenics training, athletes' movements need to be monitored and analyzed. The traditional monitoring method has the problem of insufficient resolution, which limits the improvement of accuracy and reliability. In this paper, a high dimensional image super resolution model is developed by using infrared spectral imaging technology to improve the monitoring and simulation results in the process of aerobics training. In this paper, infrared spectral imaging technology combined with deep learning algorithm is used to design a high dimensional image super resolution model. Infrared spectral imaging data were collected during aerobics training, and the data were trained and learned to extract features and realize super-resolution reconstruction. Finally, the model is tested and verified to evaluate its monitoring and simulation effect in aerobics training. The experimental results show that the high-dimensional image super resolution model has good monitoring and simulation effects in aerobics training. By increasing the detail and clarity of the image, the model can provide more accurate motion analysis and motion suggestions, so as to help the trainer improve the movement skills and improve the training effect. [ABSTRACT FROM AUTHOR]

Traditional aerobics gesture recognition has shortcomings such as low scalability, limitations in application scenarios, limitations in labor costs, and human–computer interaction. In order to improve the efficiency of aerobics posture recognition, based on neural network, this paper constructs aerobics posture recognition model combined with sensor network. Moreover, aiming at the unsatisfactory performance of the natural feature-based visual 3D registration method for scenes with sparse texture features and complex dynamic scenes, a deep neural network based on CNN + LSTM is proposed to establish the relative motion relationship between camera positions in continuous video sequences. In addition, this paper uses the transfer learning method to apply the network training parameters for the classification task to the video sequence posture recognition in this paper. Finally, considering the temporal correlation between video sequences, this paper uses the LSTM structure to store long-term image memory information. The experimental results show that the performance of the model constructed in this paper is good. [ABSTRACT FROM AUTHOR]

In the aerobics competition, the judge's judgment plays a key role in the athlete's performance and ranking. However, due to the existence of human factors and subjective judgments, artificial judgments are prone to errors and unfairness. Therefore, the purpose of this study is to design and implement a machine vision system based on light detection sensor, which is used to assist judges in aerobics competition. The system aims to improve the objectivity and accuracy of competition judgment and reduce the influence of subjective factors on competition results. In this paper, the optical detection sensor technology is used to obtain real-time movement data of athletes by setting sensors in different positions of the field. The sensors can recognize the athlete's body outline, posture and movement flow, and transmit the data to a computer for real-time analysis and processing. Through machine vision algorithms, the system can automatically identify and analyze the degree of completion and technical difficulty of different actions. The experimental results show that the machine vision system based on the light detection sensor can accurately capture the movements of athletes and provide reliable data for judging. Compared with traditional manual evaluation, this system has higher objectivity and accuracy, and can reduce the interference of subjective factors. [ABSTRACT FROM AUTHOR]

Aerobics is a difficult gymnastics event, which requires very high skills for athletes. At present, the traditional video analysis method is mostly used in the simulation system of aerobics training management competition, which has some problems such as low analysis efficiency and complex data processing. Therefore, the purpose of this study is to design and implement an optical imaging detection method based on data information visualization, so as to provide an efficient and accurate aerobics training management competition simulation system. In this paper, an aerobics training management competition simulation system is designed by using optical imaging technology and data information visualization method. The system monitors and records athletes' key movements in real time through camera equipment, and then transforms the monitored movement data into intuitive visual images by using data information visualization technology. The application effect of the developed optical imaging detection method based on data information visualization in the simulation system of aerobics training management competition is verified by experiments. The experimental results show that the system can effectively and accurately monitor the key movements of athletes, and display and analyze them through intuitive visual images. [ABSTRACT FROM AUTHOR]

Due to the current video key frame extraction algorithm is affected by the lens conversion, the extraction accuracy is poor. For this reason, a precise extraction algorithm of video action key frames for online aerobics teaching is studied. According to the color components of the video color RGB space, in order to ensure that the color distance is suitable for human vision. A non-uniform quantized HSV space method is adopted, and a one-dimensional feature vector is introduced to convert the online teaching video of aerobics into a one-dimensional histogram of 72 bins, which realizes the segmentation of video shots and reduces the impact of shot conversion. Sort the gray values of the histogram pixels of the video after the segmentation is completed, and construct the dynamic frames of the aerobics online teaching video. Sequence search constructs the processing dynamic frame, extracts the feature vector of the video sequence, and uses the multi-layer core aggregation algorithm to extract the key frame of the video action according to the extracted feature vector. Experimental results show that the algorithm can effectively extract the key frames of aerobics video action, the fidelity of the extracted key frames is higher than 0.9, and the precision and recall are both higher than 99%. [ABSTRACT FROM AUTHOR]

Fibromyalgia syndrome, a chronic condition typically characterized by widespread pain, nonrestorative sleep, fatigue, cognitive dysfunction, and other somatic symptoms, negatively impacts physical and emotional function and reduces quality of life. Exercise is commonly recommended in the management of people with fibromyalgia, and interest in examining exercise benefits for those with the syndrome has grown substantially over the past 25 years. Research supports aerobic and strength training to improve physical fitness and function, reduce fibromyalgia symptoms, and improve quality of life. However, other forms of exercise (e.g., tai chi, yoga, Nordic walking, vibration techniques) and lifestyle physical activity also have been investigated to determine their effects. This paper highlights findings from recent randomized controlled trials and reviews of exercise for people with fibromyalgia, and includes information regarding factors that influence response and adherence to exercise to assist clinicians with exercise and physical activity prescription decision-making to optimize health and well-being. [ABSTRACT FROM AUTHOR]

When analyzing the environmental development of coastal areas, the main basis used by researchers for analysis is the data information collected by remote sensing sensors. In the process of research and analysis, experts divided the areas displayed by remote sensing images into three types based on the characteristics of ocean areas, land, and intermediate zones, namely, sea areas, coastal beaches, and land areas. Today, with the continuous development of science and technology, people can choose the most appropriate image research method according to the development characteristics of different regions to better study the spatial distribution and geographic distribution of images. In the current era of development, the level of development of robot services is constantly improving. In order to improve the intelligence of robot service functions, people have begun to study the ability of machines to recognize human activities, and strive to meet the needs of development in all aspects. This article analyzes the characteristics of robot visual action recognition, compares and analyzes three different action representation methods, and uses deep learning related technologies to improve the level of action recognition. [ABSTRACT FROM AUTHOR]

Currently, radial basis functions and support vector machines (SVM) are the best methods for big data modeling. SVM can approach any non-linear variable relationship with arbitrary precision, and deal with the complex laws between variables more appropriately. It provides new ideas and methods for the development of predictive models. The predictive accuracy is good, and all the results are satisfactory. Rivers, seas, and lakes accumulate sediments due to various objective reasons such as nature, and these materials may be deposited on land or in the ocean. Terrestrial deposits are formed on land, but sometimes they also accumulate on land, oceans, or lakes. Accumulation is the raw material of accumulation rock and may contain fossils of aquatic organisms. When these aquatic creatures die, they will be covered by accumulations. The deposits at the bottom of the lake that are not fossilized can be used to infer the previous climatic conditions. The university's sports major is the training base for sports talents. With the progress of China's school sports reform, the society urgently needs new sports talents with a solid foundation, extensive capabilities, strong self-confidence, and great innovation capabilities. Aerobics is a new sport in Chinese sports. It entered university physical education in the middle of the last century. It is now loved by most teachers and students and has become a common sport in university physical education. The physical education subjects of universities in the A area generally provide aerobics professional courses. With the deepening of education reform, many problems have appeared in the education content, which directly affects the training of bodybuilding talents and the sustainable development of aerobics. [ABSTRACT FROM AUTHOR]

Direct signals from the global navigation satellite system have reflection and scattering effects after passing through the sea level. When the reflected signal and the scattered signal enter the receiver, multipath effects will occur, which will affect the measurement results. Therefore, in navigation and positioning measurement, such signals must be suppressed. With the steady progress of global navigation satellite systems (GNSS) systems, reflected or scattered signals can be used to remotely sense the height of the sea. This altimeter technology is called global navigation satellite system reflectometry (GNSS-R). As a new sea altimeter technology, it can make up for the shortcomings of the high-cost satellite altimeter technology and the narrow range of tides in the previous sea altimeter technology, providing long-term stability and high spatial and temporal resolution signals. As a station altimeter, it is expected to solve the problem of medium-scale sea level altimeters. GNSS-R altimeter technology is one of the research fields that attract the attention of domestic and foreign experts and scholars. As an important part of college physical education curriculum, aerobics plays an important role in guiding students' physical and mental development and improving students' esthetic ability. While comprehensively popularizing high-quality education, the goal of comprehensive training of students has also been achieved. Different from the previous sports aerobics courses, if you want the continuous and diverse development of aerobics, you must continue to promote the reform and optimization of gymnastics guidance, the update of course content, and the guidance methods. In the development of aerobics research and practice, new experiences and social needs will be transformed into education and the quality of education will be improved. This article analyzes the factors that affect the quality of aerobics education in coastal areas and puts forward corresponding countermeasures and suggestions to improve the quality of aerobics education. [ABSTRACT FROM AUTHOR]

Exercise is one of the most discussed and controversial nonpharmacologic management strategies for osteoarthritis (OA) of the knee. Health care providers and patients share varied and often pseudoscientific beliefs regarding the effects of exercise on knee OA formulated on outdated notions of the etiology, pathophysiology, and progression of the condition. Based on the contemporary literature, regular light to moderate physical activity has both preventive and therapeutic benefits for individuals with knee OA. Exercise regimens with strong evidence of benefit include those that focus on aerobic/cardiovascular conditioning and lower extremity strength training. Health care providers should confidently incorporate exercise recommendations into clinical management and offer patients evidence-based and individually tailored exercise prescriptions to help manage the painful and often disabling symptoms of this condition. [ABSTRACT FROM AUTHOR]