Entertainment Computing最新文献

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.

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

In the digital era, online reactions to broadcast media are an important feature of audience engagement. Although central, the question of the temporality of audience online reactions has been understudied. We investigate this question by exploring the temporal patterns of online reactions to fictional characters’ deaths. More than 3,500 forum reactions to Game of Thrones characters’ deaths were collected over 5 years. Temporal patterns of reactions to expected deaths displayed more long-term patterns, while reactions to unexpected deaths displayed more spontaneous patterns. These results further our understanding of death reactions’ temporality in cyberspace, in a multimedia and transmedia storytelling context.

This study designs an entertainment interactive robot system for gaming psychotherapy intervention and evaluates its effectiveness in preventing depression in students. In terms of the architecture and hardware of the entertainment interactive robot system, the robot adopts high-performance processors and sensors to achieve the ability to quickly respond and accurately perceive user actions. In terms of software, a mathematical model was established based on the structure and kinematic principles of the robot to describe its motion. Preprocess user gesture data by analyzing the features and patterns of user gestures, extracting key action information, and using machine learning algorithms or deep learning models to classify and recognize gestures to determine user intent and action types. Evaluate action recognition and effectiveness, and verify the accuracy and reliability of the gesture interaction module through comparison and testing with actual user gestures. The study designed an entertainment interactive robot assisted game that provides a fun and interactive experience to attract students’ attention and stimulate positive emotions. Through game design and behavioral pattern evaluation, the research team evaluated the preventive effect of entertainment interactive robot assisted games on depression and proposed optimization strategies.

With the rapid development of digital entertainment platforms and immersive virtual reality (VR) technology, the visualization of environmental landscape design has attracted widespread attention. This study aims to explore the visualization methods and effects of environmental landscape design based on digital entertainment platforms and immersive VR experiences. By capturing three-dimensional point information in the environment, high-precision environmental data can be obtained. Next, convert the point cloud data into a digital model with terrain features, and use voxel scene construction technology to further transform the terrain model into a voxelated digital model. In the design process of landscape design systems based on digital entertainment platforms and immersive VR experiences, various interaction methods have been implemented, such as gesture recognition, head tracking, and touch control, so that users can freely browse and operate virtual landscapes. By introducing landscape design principles and rules, users can modify and customize the landscape in a virtual environment. The research results indicate that the visualization method of environmental landscape design based on digital entertainment platforms and immersive VR experience can effectively display the characteristics and details of the environmental landscape, improve user perception and participation.

With the development of educational technology and the arrival of the intelligent era, intelligent entertainment voice robots have gradually become a research hotspot in the field of education. This study aims to explore the application of intelligent entertainment voice robots based on data mining in the promotion of personalized English learning, and combine with gamified virtual teaching to improve learners’ learning interest and learning effect. The study uses data mining technology to analyze learners’ learning data, interest preferences and learning performance, and provides learners with targeted learning resources and gamified virtual teaching environment based on personalized recommendation. The study creates a virtual teaching environment presented in the form of games, which contains various interesting tasks, challenges and reward mechanisms. Learners can participate in the virtual teaching environment at the same time, experience the fun and stimulation of games, so as to stimulate the interest and motivation of learning. Learners can engage in interactive speech learning through conversations with intelligent entertainment speech robots. The robot can provide real-time answers to learners’ questions, provide personalized learning suggestions, and provide encouragement and feedback. By interacting with robots, learners can get more flexible and personalized learning support, improving learning effectiveness and satisfaction. Through experiments and data analysis, it is found that intelligent entertainment voice robot based on data mining and gamification teaching experience can effectively improve learners’ learning interest and learning effect. Learners show higher engagement and motivation in personalized recommended learning resources and gamified virtual teaching environments.

In the field of social robot teaching, research has focused on how to use technological means to provide better learning support and personalized interactive experiences. Social robots can interact with students and provide personalized learning support, thereby improving their learning effectiveness and engagement. The speech sensing model of social robots can perceive students’ emotions and feedback in real-time through technologies such as speech recognition and sentiment analysis, thereby providing intelligent responses and guidance. The deep learning recommendation model for music course resources extracts music features through deep learning techniques, and combines session interest extraction techniques to personalized recommend music resources suitable for students’ interests and abilities. By analyzing students’ interests and learning goals, robots can provide music learning resources that meet their needs based on recommendation algorithms, further stimulating their learning interest and enthusiasm. The experimental results show that the use of social robots in the learning environment significantly improves the learning effectiveness and participation of students. Through personalized interaction and intelligent response guidance, students are more likely to understand and master music knowledge, while experiencing joyful and positive learning emotions. The study validated the effectiveness of social robot assisted music courses based on speech sensing and deep learning algorithms, demonstrating its advantages in improving student learning effectiveness and engagement.