Research on the Development of Digital Creative Sports Industry Based on Deep Learning

The core of the digital entrepreneurial sports culture creative industry lies in innovation, which emphasizes the new impetus brought by the digital entrepreneurial sports culture to the social economy. The digital entrepreneurial sports cultural creative industry is rooted in the cultural creative industry. The digital entrepreneurial sports cultural creative industry is also an important part of the sports industry, and its development highly depends on the development of the sports industry. The digital entrepreneurial sports cultural creative industry has the characteristics of both the sports industry and the cultural creative industry. This paper uses the deep learning technology to study the development of the digital creative sports industry and build an intelligent model. Moreover, this paper assigns weights to the input multidimensional features, extracts the most relevant data features, and analyzes the performance of the proposed model through simulation experiments. From the experimental analysis results, we can see that the model proposed in this paper has certain practicality.

Professional Self-Concept and Self-Confidence for Nurses Dealing with COVID-19 Patients

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses’ professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19- and non-COVID-19-designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses’ Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the group exposed to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients.

Test–Retest Reliability and Minimum Detectable Change of the Athletic Trainers’ Self-Confidence Scale

The Athletic Trainers’ Self-Confidence Scale (ATSCS) is a nine-item Likert-scale questionnaire assessing the respondent’s level of agreement with statements regarding confidence in recognizing and managing exertional heat illnesses. Test–retest reliability of this instrument has not yet been established. The purpose of this study was to investigate the internal consistency, test–retest reliability, and minimum detectable change score for the composite score of the ATSCS. A total of 18 professional master of science in athletic training students (nine first-year and nine second-year students) completed the ATSCS at three testing sessions with 48 hr between sessions. The nine items of the ATSCS demonstrated good internal consistency (α = .86; 95% confidence interval [.78, .94]). The composite scores of the ATSCS demonstrated moderate test–retest reliability (intraclass correlation coefficient = .75; 95% confidence interval [.497, .893]). The calculated minimal detectable change for the composite change score was 6.19. The ATSCS has good internal reliability as well as test–retest reliability. These results display that the tool will provide consistent, reliable results of changes in athletic training students’ self-confidence.

Hybrid fuzzy interface model of sports rehabilitation activities

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

Exploring nursing students' experiences of blindness simulation: A phenomenological study

Aim

The aim of this study was to explore the experiences of blindness simulation among a group of nursing students.

Design

This qualitative study was conducted using an interpretive phenomenological method.

Methods

Using purposeful sampling method, students were informed and invited to participate in the research through the Website of Tehran School of Nursing and Midwifery. We listed the candidates and until data saturation and compilation of the study, 8 students entered the study. They shared their experiences about blindness simulation through individual in‐depth and semi‐structured interviews. We continued the interviews until the data were saturated. The transcribed interviews were analysed by Colaizzi's approach.

Results

The results of data analysis resulted in three major themes and nine sub‐themes, which were conceptually named based on their nature. The major themes included abandoned in the labyrinth puzzle, vision of heart and self‐alienation.

Design of evaluation system of physical education based on machine learning algorithm and SVM

The evaluation system of physical education is limited by many factors, so the reliability of the quantitative results of its intelligent scoring system is not high. In order to improve the teachingeffect ofphysical education major, this paper combines a machine learning algorithm and SVM to build anevaluation system of physical education. The system uses optimized machine learning as the system algorithm. In order to improve the operating efficiency of the system, this study optimizes the system physical layer certification to improve the system data processing speed and accuracy and uses a three-layer structure to build a basic model of the system structure and analyze its functional modules. Moreover, this study uses a database based on an expert evaluation system for data processing to achieve physical education evaluation and puts forward corresponding improvements. In addition, system performance verification is carried out on the basis of building the system. Through various experimental verifications, we know that the model constructed in this paper has good performance and can be applied to actual physical education.

Research on sports simulation and fatigue characteristics of athletes based on machine learning

The research on the fatigue characteristics of athletes has a certain role in promoting the development of sports. In order to detect fatigue more accurately in the state of human fatigue, this article uses a method of fusing characteristic information of many physiological parameters related to fatigue to design a multi-physical parameter-based exercise fatigue recognition method with high research value and significance. Moreover, this study combines machine learning technology to construct a dynamic fatigue detection system based on BP neural network and multiple physiological parameters. In addition, this study uses samples to construct a BP neural network and achieves dynamic detection of fatigue through multiple physiological parameters. Finally, by constructing controlled trials, fatigue is predicted. The results show that the predicted output of the fatigue value is in good agreement with the expected output, and the research method has certain practical effects.

Application of VR image recognition and digital twins in artistic gymnastics courses

Because rhythmic gymnastics requires a combination of human movements and hand-held instruments, it is difficult to teach and requires high movement standards. Therefore, the actual course teaching is difficult. In order to improve the teaching efficiency of rhythmic gymnastics courses, based on VR image recognition technology and digital twins, this paper combines the actual teaching needs of rhythmic gymnastics to build a corresponding auxiliary teaching system. The sports database designed in this article mainly has three kinds of sports: difficulty movements, connecting movements and equipment movements. It is different from the traditional method in that each movement and the device-related connection movement correspond to a difficulty movement of the same length and close coordination, and the connection movement plays a role in smoothly connecting the two difficulty movements. In addition, the performance of the auxiliary teaching system constructed in this paper is studied through system experiments. The research results show that this system is feasible.

Research on college physical education model based on virtual crowd simulation and digital media

At present, the traditional sports teaching model has been unable to meet the needs of modern diversified talents. Based on the actual needs of physical education, this article reforms traditional physical education methods, proposes a university physical education model based on virtual crowd simulation and digital media, and builds a corresponding system. Moreover, this paper uses feature identification to monitor classroom teaching and counts multiple parameters to assist the effective teaching activities. In model tracking, the DMS motion trajectory is obtained, and then the center points of all grid sequences are calculated to generate a center point matrix. Considering the difference in motion between adjacent frames of DMS, the difference is quantified by the significance value, so that the sequence of adjacent frames with small differences is organized into clusters. In addition, this paper builds a computer system model based on actual needs and evaluates model performance through actual teaching. The research results show that the proposed model has good performance.

Image detection and basketball training performance simulation based on improved machine learning

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.

Machine learning model for feature recognition of sports competition based on improved TLD algorithm

Sports competition characteristics play an important role in judging the fairness of the game and improving the skills of the athletes. At present, the feature recognition of sports competition is affected by the environmental background, which causes problems in feature recognition. In order to improve the effect of feature recognition of sports competition, this study improves the TLD algorithm, and uses machine learning to build a feature recognition model of sports competition based on the improved TLD algorithm. Moreover, this study applies the TLD algorithm to the long-term pedestrian tracking of PTZ cameras. In view of the shortcomings of the TLD algorithm, this study improves the TLD algorithm. In addition, the improved TLD algorithm is experimentally analyzed on a standard data set, and the improved TLD algorithm is experimentally verified. Finally, the experimental results are visually represented by mathematical statistics methods. The research shows that the method proposed by this paper has certain effects.

An artificial intelligence fuzzy system for improvement of physical education teaching method

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.

An artificial intelligence and machine vision based evaluation of physical education teaching

The manual evaluation method to evaluate the effect of physical education teaching is tedious, and it will have a large error when the amount of data is large. In order to improve the efficiency of physical education evaluation, this article uses artificial intelligence for data analysis and uses machine vision to identify the teaching process to assist teachers in physical education. In order to reduce the calibration error of the parameters and obtain more accurate camera imaging geometric parameters, this paper adopts the method of averaging multiple sample points to determine the calibration parameters of the camera. In addition, this study builds system function modules according to actual needs and verifies system performance through experimental teaching methods. The research results show that the model proposed in this paper has a certain practical effect.

Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model

The problems and disadvantages of the traditional teaching mode of Taekwondo in colleges and universities are obvious, which is not conducive to cultivating the interest of contemporary college students in learning Taekwondo. In order to improve the teaching effect of Taekwondo, based on the intelligent algorithm of human body feature recognition, this study uses support vector machine to construct a Taekwondo teaching effect evaluation model based on artificial intelligence algorithm. The model corrects the movement of the students by recognizing the movement characteristics of the students’ Taekwondo and can conduct the movement guidance and exercises through the simulation method. In order to verify the performance of the model in this study, this study set up control experiments and mathematical statistical methods to verify the performance of the model. The research results show that the model proposed in this paper has a certain effect and can be applied to teaching practice

To strengthen self-confidence as a step in improving prehospital youth laymen basic life support

Background

A rapid emergency care intervention can prevent the cardiac arrest from resulting in death. In order for Cardio Pulmonary Resuscitation (CPR) to have any real significance for the survival of the patient, it requires an educational effort educating the large masses of people of whom the youth is an important part. The aim of this study was to investigate the effect of a two-hour education intervention for youth regarding their self-confidence in performing Adult Basic Life Support (BLS).

Methods

A quantitative approach where data consist of a pre- and post-rating of seven statements by 50 participants during an intervention by means of BLS theoretical and practical education.

Results

The two-hour training resulted in a significant improvement in the participants’ self-confidence in identifying a cardiac arrest (pre 51, post 90), to perform compressions (pre 65, post 91) and ventilations (pre 64, post 86) and use a defibrillator (pre 61, post 81). In addition, to have the self-confidence to be able to perform, and to actually perform, first aid to a person suffering from a traumatic event was significantly improved (pre 54, post 89).

Conclusion

By providing youth with short education sessions in CPR, their self-confidence can be improved. This can lead to an increased will and ability to identify a cardiac arrest and to begin compressions and ventilations. This also includes having the confidence using a defibrillator. Short education sessions in first aid can also lead to increased self-confidence, resulting in young people considering themselves able to perform first aid to a person suffering from a traumatic event. This, in turn, results in young people perceiveing themselves as willing to commence an intervention during a traumatic event. In summary, when the youth believe in their own knowledge, they will dare to intervene.

Developing Cognitive Skills Through Active Learning: A Systematic Review of Health Care Professions

Objective

To systematically review current literature to determine whether active learning is more successful than passive learning at producing cognitive skills in health care professions students.

Data Sources

An electronic search was conducted in 4 databases: EBSCO-CINAHL, EBSCO-Sport Discus, Educational Resources Information Center, and PubMed. Search terms included: millennial AND health education, active learning AND knowledge retention, flipped classroom AND learning outcomes, problem based learning AND learning outcomes, problem based learning AND student confidence, active learning AND critical thinking, higher order thinking AND active learning.

Study Selection

We included studies if they were published in English between 2007 and 2017 and evaluated outcomes of an active learning intervention. Studies of nonhealth care disciplines, practicing health care practitioners, or studies that did not address the primary research questions were excluded.

Data Extraction

Study design, health care discipline, intervention used, assessment measures, outcome(s) measures, main results, and conclusions were extracted from each article, as appropriate.

Data Synthesis

Articles were categorized based on capacity to answer 1 or both of the research questions. Conclusions were summarized according to the learning technique used and its effectiveness in regard to studied learning outcome. Out of 85 studies on lower-order cognition, 61 (72%) indicated active learning techniques were effective at achieving improved recall, understanding, and/or application of course material. Of 69 studies on higher-order cognition, 58 (84%) supported active learning over passive instruction for improving students' confidence in or performance of analytical, evaluative, and creative skills.

Conclusions

Active learning produces gains to both lower- and higher-order cognition at levels equal to, and more often, greater than the use of passive learning methods. Despite this evidence, we believe more high-quality, well-designed prospective studies using validated assessment measures are needed to endorse the value of these methods in producing cognitive skills.