Mobile Networks and Applications最新文献

In recent years, the Internet of Things (IoT), Machine Learning (ML), and Big Data (BD) technologies have played important roles in progressing healthcare and stress management solutions. The technology allows for constant supervision of patients’ conditions, immediate data analysis, and individualized treatment courses by improving healthcare effectiveness in treating numerous health challenges. When examining physiological stress in college students, the stress level can influence students’ results and well-being. Given these challenges, this paper proposed a new IoT-based system utilizing ML and BD techniques, specifically the Mixed-Density Neural Networks (MDNN) technique, for stress improvement through musical therapy. The proposed MDNN incorporates several neural network structures to perform and analyze numerous input signals by making it individualized and consistently delivering therapeutic music. The suggested study commences by compiling various datasets involving data from microphones, physiological signals, and the environment, as these datasets are crucial for developing a holistic approach that understands and eradicates stress through music therapy. After that, the proposed work examines other methods used in feature extraction to process and analyze this data, which is vital in improving the performance of the MDNN model. The suggested MDNN employs several neural network structures to process the multi-modal inputs by allowing the real-time adjustment of therapeutic music based on the user’s stress level. Experimental results highlight the MDNN’s impressive performance metrics: accuracy, sensitivity, specificity precision, F1-score, and MCC 90.38%, 91.20%, 89.50%, 88.75%, 89.95%, and 0.82%, respectively. Moreover, the results show minimal error metrics with MAS RMSE Huber Loss and MAE, 0.15, 0.20, 0.18, 0.12. Comparative analysis against traditional machine learning models consistently shows the MDNN’s superior performance by indicating its potential to innovate stress management via personalized music therapy in educational backgrounds.

In recent years, technology development has deeply impacted numerous sectors, including education. Innovations such as the Internet of Things (IoT) and Artificial Intelligence (AI) have revolutionized teaching methods, presenting personalized learning knowledge and enhancing educational results. These technologies have enabled teachers to modify lessons to specific student requirements, track progress in real-time, and provide interactive learning environments that promote engagement and retention. To address the developing educational environment these technologies allow, this paper proposed an innovative framework that integrates IoT-enabled mental health based on deep learning techniques for students of English teaching using generative adversarial networks (GANs) algorithm for personalized educational involvements. IoT devices for the entire data-gathering approach incorporate academic records and real-time mental health indices through the framework to assist educators in understanding how their students function and feel about learning. GANs handle and analyze this rather diverse data set and generate feedback and learning strategies based on students’ specific profiles. Such an integration proves to be maximally effective in increasing compliance with educational interventions while at the same time promoting the students’ all-rounded development by fulfilling their academic, emotional, and social learning requirements. The experimental results achieved superior performance with an accuracy of (0.916%), an F1 score of (0.921%), and an MCC of (0.829), and the error metrics include MAE of (0.12), MSE of (0.25), RMSE of (0.27), and MAPE of (0.31), respectively. The proposed model outperforms traditional machine learning techniques such as DNN, RNN, LSTM, and CNN, highlighting its superior predictive performance in educational mental health for English teaching applications. Moreover, the paper examines the importance of ethical considerations, educational psychology, and future research directions, emphasizing the transformative possibility of IoT and GAN technologies in proffering personalized learning methodologies in education.

Injuries are a big concern in professional sports. It is recognized as one of the significant factors in athletes’ careers and team performance. Early detection of injuries in sports can assist teams in taking preventive measures and enhance player’s performance. This paper explores the use of machine learning algorithm namely Support Vector Machines (SVMs) to predict injuries in professional sports and use Big Data Analytics (BDA) techniques to provide useful insights regarding players. SVMs are capable of handling complex and non-linear relationships among data and classifying it accurately while BDA aids in player health management and resource allocation The study commences by collecting large amounts of data from various sources related to athletes and storing it in Cassandra. These sources include athlete performance records, medical histories and wearable technology data. The data is then cleaned and transformed into a uniform format for processing. The Recursive Feature Elimination (RFE) technique is used to pick the most relevant data points. These tools are pivotal in handling the volume, velocity and variety of the data. Secondly, an SVM model is formulated which includes input features, kernel functions and a decision function. The model works by mapping input data into a high-dimensional space using the kernel function. It then finds the optimal hyperplane that maximizes the margin between the two classes which are injured and not injured. The data points closest to the hyperplane are represented in the form of support vectors and are used to predict new data points and classify the vector as injury or non-injury. Finally, the proposed SVM model is trained on a subset of the data. It uses grid search and cross-validation techniques to optimize the model’s performance. The results show that the proposed SVM model achieved an accuracy of 92.3% and a prediction rate of 87.5%, which highlights the effectiveness of our approach.

The advancement of AI is opening gateways for sports analytics and sports healthcare. This paper investigates the use of Reinforcement learning alongside IoT devices to establish optimum policy in coaching. The optimum policy will cover three aspects of the agent, (1) the attacking position (2) the defensive position (3) the health of the agent both in defensive and attacking mode. This paper also investigates the training strategies of basketball to enhance player movement and health life. A DQN approach along with an IoT health sensor is used in simulation settings. The sensors in the simulation were attached to an agent to record the data of the agent related to health. The simulation analyzes the movement of players according to the conditions, the trajectory of the ball, and the health condition of the players. Based on this condition the player creates defensive and attacking strategies by shifting positions. The received data is passed through a neural network architecture to maximize the performance of the player and increase the play life and performance of the player. Different parameters of Deep Q-learning such as reward shaping Learning rate and loss function of the model. This multidisciplinary approach focuses on automated decision-making processes and flexible methodologies tailored to dynamic game situations, to connect concepts from healthcare analytics to sports training. The study proposes new methods for assessing player performance, anticipating game outcomes, and developing effective training regimens based on ideas from IoT-enabled healthcare, such as real-time monitoring and predictive analytics. Our model simulation integrated with deep learning demonstrates substantial improvement in playing court. The model predicts 95% accuracy predicting accurate moves both in attacking and defensive positions. The risk of injury is reduced by up to 60% and the overall performance and efficiency of the player was 98% in gameplay.

Psychological health is crucial in educational settings and recognized as a significant feature in structuring behavior of teachers and learning outcomes of students. Timely and accurate identification of mental health issues aids in early intervention and initiation of the recovery process. Traditional assessment methods are subjective, time-consuming and faced different challenges. This study uses Recurrent Neural Networks (RNNs) to evaluate the psychological condition of students of English language. RNNs uses Long Short-Term Memory (LSTM) layers to capture long-term dependencies in language and develop a robust and efficient model that assesses students' psychological well-being through their written and spoken English. The RNN architecture is composed of several components. Firstly, it has an embedding layer that converts words into dense vectors of fixed size. Next, two stacked LSTM layers process these vectors and capture contextual information from the sequences followed by fully connected dense layers which transform LSTM outputs into psychological health scores. Finally, a sigmoid activation function in the output layer classifies the psychological state such as signs of stress or no stress. The data for this study includes essays, classroom discussions and interactions from English language learners. The data is preprocessed with tokenization, lemmatization and removal of stop words. To demonstrate the performance of RNN in forecasting English language student’s mental health it is compared with different state of the art algorithms like Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Random Forests (RF) in terms of accuracy, precision, recall, and F1-score. The results show high accuracy in predicting stress, anxiety and motivation levels outperforming its predecessors and leading to better teaching strategies and improved learning outcomes.

Internet of Things (IoT) technology has been adopted football Practice industry, where athletes train and upgrade their health status. Internet-connected machinery has the potential to gather huge amounts of data in real-time personal characteristics of an individual athlete; his or her, motion, health, and other parameters and conditions of the surrounding environment. This information, which is not obvious in the traditional training techniques can be very valuable in the individualization of training processes. In basketball, where skillful maneuvers, accuracy, speed as well as planned movements are important IoT technology can be of great importance. The paper outlines a method in which basketball players are furnished with IoT gadgets that may monitor activities such as pulse rate, oxygen level, and movements. It is essential to note that these devices participate in data transmission to a central system where a Q-learning algorithm takes place. The algorithm’s decision-making principles are the reward functions that are prescribed to suit the most preferable behaviors: performance parameters (e.g., shooting accuracy, speed, etc.) and physiology parameters (e.g., heart rate variability, recovery rates, etc.). It is paramount that such training alterations are not only performance-oriented but also health-centered, hence maintaining a two-pronged focus on overall player growth. The outcomes demonstrate the contrast between regular mode training sessions and IoT/Q-learning enhanced training sessions and figure out the enhancement of 15% via shooting precision within six weeks. It establishes a link between adapting training sessions to the health of the players involved and the execution of the skills incorporating enhanced agility of participants by 20 percent. The ideas for the adaptive system entail immediate feedback and modification procedures, which may afford enhanced training results.

With the rapid advancement of information technology, the integration and utilization of photoelectric sensor equipment within the realm of physical education are experiencing a notable increase, particularly in the environments of colleges and universities. The widespread adoption of wireless networking technologies has ushered in revolutionary opportunities for the transmission of data and for the real-time monitoring capabilities of photoelectric sensors. This study is aimed at investigating the practical applications of photoelectric sensor devices that employ dynamic adaptive cache technology in the context of physical education instruction at higher education institutions. Additionally, it seeks to assess the impact of these technologies on teaching effectiveness and the levels of student engagement. For the purposes of this research, a specific physical education course at a university has been chosen as the focal point of the study. Within this framework, photoelectric sensor equipment configured to operate over wireless networks has been deployed, and this setup is enhanced further through the incorporation of dynamic adaptive cache technology, which facilitates the efficient management and transmission of data. Throughout the course delivery, real-time monitoring of students’ athletic performance is conducted, allowing for the collection and analysis of relevant performance data. The findings of the experimental analysis reveal that the photoelectric sensor devices—operating over wireless networks—are highly effective in capturing data pertaining to students’ movements and athletic endeavors. This data is transmitted in real time thanks to the dynamic adaptive caching technology implemented in the system, thus significantly enhancing the interactive nature and immediacy of the teaching process. As a result, there has been a marked increase in both the participation levels and enthusiasm of students in their physical activities. Moreover, the overall effect of the teaching has seen significant improvements.

Because of its non-invasive and rapid response, NIR imaging has shown great potential in the field of biological information acquisition and analysis. This study aims to explore the application of near infrared spectral imaging technology based on cloud data and wireless network sensing in big data sports fitness detection, aiming to improve the collection efficiency and analysis accuracy of sports data, so as to provide scientific basis for personal health management. In this study, near infrared spectral imaging instrument was used to collect real-time physiological data during exercise through wireless network sensing equipment. The collected data is transmitted to the cloud platform through the mobile network, and big data analysis technology is used to conduct in-depth analysis of physiological characteristics and athletic performance. Through the design of monitoring system based on the Internet of Things, the efficient collaboration between multiple devices is realized. The experimental results show that the constructed system can monitor users' physiological parameters in real time, such as blood oxygen saturation, muscle oxygenation, etc., and organize and analyze the data through the cloud platform. Compared with the traditional monitoring method, the system greatly improves the data transmission rate and processing efficiency, and effectively improves the accuracy and timeliness of physical fitness detection.

With the rapid development of the Internet of Things technology, the use of light sensing technology combined with wireless networks can collect users’ physiological data in real time to help users better manage their health. This study aims to explore the data analysis application of wearable devices based on optical sensing and wireless networks in community fitness, so as to improve the fitness participation and health management effect of community residents. The research designed a wearable device with integrated optical sensor and wireless network function, which can monitor heart rate, blood oxygen saturation and exercise status in real time. Data is uploaded to the cloud via Bluetooth and mobile networks for storage and analysis. Community users view their own data records and analysis reports through mobile applications, and the research team processes the collected data through big data analysis methods to find the connection between fitness activities and health indicators. The results of the study showed that users of the device experienced significant improvements in fitness engagement and exercise effectiveness. The user’s heart rate and blood oxygen level remained in a healthy range over multiple fitness cycles, and the analysis results indicated that regular exercise time was positively correlated with physiological health indicators. This technology not only makes data collection more convenient, but also provides personalized health management programs for community residents and promotes the development of healthy lifestyle.

With the rapid development of Internet of Things technology, the application of wireless sensor network in sports training has been paid more and more attention. As an important tool for data acquisition and monitoring, wearable devices can obtain athletes' training data in real time, providing new opportunities for the analysis and improvement of sports performance. This paper aims to explore the application of wearable devices based on wireless sensor network and voice synchronization overlay algorithm in the simulation of sports training data, aiming to improve the accuracy and real-time performance of data acquisition, and then optimize the effect of sports training. A wearable device that integrates wireless sensor network and voice processing technology is designed to collect physiological and sports data of athletes by various sensors. At the same time, the voice synchronization overlay algorithm is used to process the collected data in real time and improve the data analysis ability. By simulating the training environment, the data performance of the system in different motion scenarios is evaluated. The experimental results show that the wearable device can effectively collect the physiological data of athletes during training, including heart rate, step frequency and movement trajectory, etc., and the data processing delay is significantly reduced through the voice synchronization overlay algorithm. The system shows good stability and reliability under different training modes.