Entertainment Computing最新文献

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.

Entertainment robots, as a new type of entertainment device, have broad application prospects. Entertainment robots provide entertainment and entertainment experiences through interaction with users. This article designs a programming model to interpret and execute user gesture commands, and convert them into drawing actions that robots can process. By interacting with entertainment robots, users can guide robots to draw through gestures, making artistic creations more intuitive and interesting. We used deep learning algorithms for training and used existing art works as references to enable robots to learn and imitate the painting styles of different artists. Finally, by optimizing the algorithm, the optimal path for the entertainment robot to draw trajectories was determined, which improved the effectiveness and quality of the painting. Through the training of deep learning algorithms, entertainment robots can capture the characteristics and details of an artist’s painting style, and simulate it during the painting process. This provides users with a personalized artistic creation experience, allowing them to interact with entertainment robots, participate in artistic creation, and experience a creative process similar to that of real artists.

The Adaptive Mixed Reality Robot Games (AMRRG) framework represents an innovative approach to integrating consumer robots into public spaces for personalized and engaging entertainment. AMRRG uses a combination of mixed reality headsets, robot movement, and interactive objects. These responsive entertainment environments need a new way to tell their story. Admixing game mechanics algorithm utilizes the world’s most advanced depth perception, computer vision, and simultaneous mapping and localization (SLAM) to enable effective distinguishing between player spaces, mixed reality areas, and robot paths for safe interaction. Reinforcement learning enables real-time adaptation of game difficulty and gameplay mechanics as players react to it. At 35 % higher than traditional video-game installations in a 5 m × 5 m environment accommodating up to 20 people, adaptive algorithms recorded efficiency values of 92 % and responsiveness of 98 %. The AMRRG framework brings home consumer robot platforms to deliver a happy gaming experience. Future research will explore the potential of AMRRG beyond simple adaptation, extending its use to therapy and education.

Currently, the application of artificial intelligence entertainment robots in park plant landscape design has attracted increasing attention. This study aims to design an artificial intelligence entertainment robot that can provide a high-quality user experience. Through virtual reality and robotics technology, designers can be provided with visual and entertaining design solutions, and more interactive experiences can be provided for design clients. Convolutional neural networks can effectively extract features from images, and utilizing spectral feature extraction technology to further improve the accuracy of image recognition. Subsequently, this study designed a robot control system and calibrated the hand eye system. The robot control system can coordinate the various functions of the robot and ensure its smooth operation in the park plant landscape design. The calibration of the hand eye system is to ensure that the robot can accurately perceive the environment and locate its own position. Through real-time control strategies, robots can respond and adjust in a timely manner based on current environmental changes and user needs. By comparing with the actual position on the ground, the accuracy of robot positioning is obtained, and the system is further optimized and improved.

The lag in course construction has led to some teaching content and methods not being well adapted to the characteristics of online teaching. Collected student face data and advanced facial recognition algorithms to automatically recognize student avatars, ensuring the accuracy of each student’s identity. During the course, facial recognition attendance technology will automatically recognize students’ attendance status and record it, thereby obtaining attendance data at any time during the teaching process. The attendance records of students are generated in real-time and easily imported into the academic affairs system for management and statistics. By applying facial recognition attendance technology, teachers can understand students’ attendance in real-time and take timely measures to improve their learning enthusiasm. Students also use this technology to more conveniently sign in, reducing potential omissions and errors in the attendance process.