Entertainment Computing最新文献

In recent years, due to the increasing number of basketball games and training activities, basketball players often face the problem of fatigue and injury. This study aims to develop an interactive digital entertainment and gamification training method to provide basketball players with more efficient training methods and enhance their training fun. Sensors can be installed in various key parts of the athlete’s body, through the use of high-precision sensor equipment, real-time acquisition of athlete action data, the use of machine learning and data mining technology, real-time analysis and modeling of sensor data to extract key information and movement patterns. Using gamified training theory to design interactive digital entertainment system, develop a series of training scenarios and gamified tasks for different technical elements, combine sensor data and motion analysis results, and provide athletes with personalized training plans and feedback mechanisms to help them improve their technical level. Through an interactive digital entertainment system, basketball players can train and compete in a virtual environment. They can complete various training tasks by interacting with the system, which will give real-time evaluation and guidance based on the athlete’s performance to help them correct mistakes, improve technique and improve training results.

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

At present, the mental health problems of college students are becoming more and more prominent, but the existing mental health detection methods have certain limitations. Therefore, the combination of entertainment and game experience with artificial intelligence technology in this paper can provide a more comprehensive and accurate mental health detection scheme. The research designed an immersive entertainment game experience platform, in which artificial intelligence technology was used to detect mental health. The platform is based on emotion recognition algorithms and artificial intelligence interaction technology to assess the level of mental health of users by analyzing their behavior and emotional expression in the game. The results show that immersive artificial intelligence technology based on entertainment and game experience can effectively simulate the mental health state of college students. Compared with traditional mental health assessment methods, this technique has obvious advantages in terms of accuracy and comprehensiveness. Users also showed a high degree of acceptance and participation for this entertaining mental health detection method. This technology can provide students with a more relaxed and interesting mental health assessment experience, and can also provide accurate and comprehensive assessment results, and provide scientific and effective guidance for mental health management and intervention of college students.

The success of baby monitoring systems is heavily dependent on their design. Well-designed systems can enhance user experience, improve accuracy, and increase trust in the technology. As a collective virtual shared space, the Metaverse technology has the potential to benefit both designers and companies involved in designing baby monitoring systems. The usefulness of the Metaverse is widely investigated in different domains. However, the adoption of the Metaverse in the design of baby monitoring systems has rarely been explored in the previous research. In addition, the integration of the Metaverse in intelligent baby monitoring systems’ design poses significant privacy challenges that need to be addressed. This study, therefore, explores the factors that impact the adoption of the Metaverse in the design of baby monitoring systems. A model is developed and tested using the Stimulus-Organism-Response (SOR) theory. The findings of the study reveal that real-time monitoring, perceived security, ease of use, and personalization positively influence the technology trust in baby monitoring systems’ design. Besides, perceived privacy positively influences the technology trust and Metaverse adoption in baby monitoring systems. On the other hand, safety concerns do not impact the technology trust or the Metaverse adoption in baby monitoring systems’ design.

Teaching games are an effective teaching organization activity. In response to the evaluation and prediction problem of teaching games, a teaching game evaluation model based on improved sparrow search algorithm and back propagation neural network was studied and constructed. Firstly, a situational teaching game was designed and an evaluation index system was constructed. Then, a teaching game evaluation prediction model based on the improved method was established. Finally, the expert consultation method is adopted to collect opinions from experts in the field of education and construct an evaluation index system for teaching games. And based on the evaluation index system of teaching games, evaluate students’ mathematical thinking ability before and after experiencing teaching games to verify the application effect of teaching games. The scenario based teaching game designed in this study has a certain effect on improving students’ mathematical thinking ability. Students’ mathematical thinking has significantly improved (P<0.05), and the teaching effect is the same for students of different genders (P>0.1). The improved sparrow search algorithm has a faster convergence rate than other algorithms, and tends to be stable when iteration is about 100 when solving the single peak benchmark function. When solving the multimodal benchmark test function, it tends to stabilize when iteration is around 20. The teaching game evaluation prediction price model based on the improved method shows a trend of first increasing and then decreasing with hidden units increasing. When the hidden unit is 16, the area index under model curve is the highest, around 0.962, and its prediction accuracy is relatively high. In summary, the model constructed in this study is applicating good in teaching game evaluation prediction, and can promote education industry developing.

Digital media entertainment technology can enhance the interactive experience of the teaching process, allowing students to engage in immersive learning in a virtual reality environment. Traditional piano teaching places too much emphasis on the classroom, and new teaching methods should be optimized accordingly. Therefore, this article further optimizes and analyzes speech algorithms and applies them to soft computing technology. It also conducts multi-dimensional testing of the upgraded speech enhancement model, aiming to design a more suitable remote piano teaching platform for students, and attempts to directly apply the enhanced speech algorithms and soft computing technology in actual teaching. Relatively speaking, the effectiveness of traditional piano classroom teaching is not significant, and the teaching results of remote piano teaching have greatly improved compared to traditional classrooms. On this basis, students can also master most of the teaching content, which is conducive to enhancing their learning interest and thereby improving their academic performance. This platform can also provide piano learners with more efficient, convenient, and practical services.

With the development of tourism industry, users have higher and higher demand for travel experience, while traditional tourism services can no longer meet the needs of users. Therefore, as an innovative tourism service mode, intelligent entertainment robots have broad application prospects. This paper proposes a design scheme of intelligent entertainment robot based on path navigation planning. By combining navigation technology and entertainment functions, intelligent entertainment robot can provide customized travel and entertainment services for users according to their needs and interests. By collecting the geographic information of the tourist scene and the user’s preference data, the path planning algorithm and machine learning technology are used to determine the robot’s cruise path and entertainment recommendation. At the same time, it also uses computer vision technology and emotion recognition technology to perceive and analyze the user’s emotional state, so as to provide a more personalized entertainment experience. The experimental results show that under the guidance and recommendation of intelligent entertainment robots, users’ travel experience has been significantly improved, and users’ satisfaction with tourism services and pleasure of entertainment experience have been improved.

With the rapid development of artificial intelligence technology, the field of sports training has also actively explored the use of artificial intelligence technology to improve training effects and experience, and virtual game experience has been widely concerned as a new training method. In order to achieve the goal of virtual game experience in sports training, this study adopts a series of methods to build a realistic virtual game platform and realize real-time interaction between athletes and virtual environment. When building a virtual game platform, the use of computer graphics technology and model modeling technology to reproduce the details of different sports scenes provides an interactive interface that enables athletes to interact with the virtual environment in a real way, such as through joysticks, motion-sensing devices or virtual reality headsets. To be able to accurately capture the athlete’s movement trajectory, the study used deep learning techniques. By embedding cameras or other sensor devices in the platform, the movement data of athletes can be obtained in real time. Then, with the help of deep learning algorithms, these data are analyzed quickly and accurately, so as to understand the athlete’s movement posture, speed, Angle and other information. The captured movement data of athletes are processed and optimized based on artificial intelligence algorithm to realize real-time interaction between athletes and virtual environment. When athletes participate in training, they receive immediate feedback and personalized training guidance, which helps to enhance the training results and experience.

With the rapid development of artificial intelligence technology, the application of interactive entertainment robots in the field of sports training has attracted wide attention. The aim of this study is to optimize the process of basketball training competition by using tracking technology through image recognition and gamification training. In this study, tracking technology is adopted to realize image recognition in basketball training competition scenes. By installing cameras or sensors and other devices, robots can capture and recognize the position, posture and movement of trainers, and can transmit these data to the robot system in real time. Through the design of interesting training games, stimulate the interest and enthusiasm of the trainers, so that they can carry out effective basketball training in entertainment. The robot system can give different rewards and feedback based on the trainer’s performance, increasing the trainer’s fun and engagement, stimulating their competitive desire and promoting the improvement of skills. The trainer can enjoy the entertainment and challenge while interacting with the robot, and maintain the enthusiasm and motivation while improving the technical level. This interactive training method can improve the monotony and boredom of traditional training, and provide the trainer with a more interesting and stimulating learning environment. The experimental results show that interactive entertainment robot combined with gamification training can effectively optimize the process of basketball training competition.

To improve the effect of ballet teaching, this study used the scientific computing model integrated with motion capture to analyze and evaluate the teaching of ballet dance postures, so as to improve the intelligence of modern ballet teaching. Moreover, this study employed the coordinate transformation and D-H method to model and analyze the forward and inverse kinematics of the ballet posture teaching model, and used the Monte Carlo method to verify the correctness of the exoskeleton motion space analysis. In addition, this study established a dynamic model, and used the Lagrangian equation method for a dynamic solution to obtain the relationship between the position, velocity and torque of each component. The data analysis indicated that the ballet posture teaching system, which is based on the scientific computing model and integrated with motion capture, can play an important role in ballet teaching.