⚡ Quick Summary
This study explores the integration of artificial intelligence (AI) in measuring exercise and fitness pressure, particularly in basketball training. Utilizing the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM), the research demonstrates enhanced accuracy in assessing athletes’ stress levels through advanced data collection techniques.
🔍 Key Details
- 📊 Focus: Basketball training and fitness pressure measurement
- ⚙️ Technology: Intelligent Physiological Monitoring Framework (IPM-EFPM)
- 🧩 AI Techniques: Long Short-Term Memory (LSTM) and machine learning algorithms
- 📈 Data Sources: Wearable sensors and real-time location systems
🔑 Key Takeaways
- 🤖 AI integration significantly improves the accuracy of fitness pressure ratings.
- 📊 The IPM-EFPM framework automates stress testing for athletes.
- 💡 Continuous validation and improvement are key components of the system.
- 🏀 Tailored applications for basketball players can enhance training programs.
- 🌐 Real-time data collection allows for immediate insights into athletes’ stress levels.
- 🔍 The study emphasizes the relationship between AI, physical activity, and psychological stress.
- 📈 Potential for broader applications in various sports and fitness settings.
📚 Background
The intersection of artificial intelligence and sports science is rapidly evolving, offering new methodologies for monitoring athletes’ performance and well-being. Traditional methods of assessing physical stress often lack precision, leading to a demand for innovative solutions that can provide real-time insights. This study addresses that need by leveraging AI technologies to enhance the accuracy of fitness pressure measurements.
🗒️ Study
Conducted by researchers Liu R and Shen W, the study focuses on the development of the IPM-EFPM, which employs complex AI algorithms to automate the assessment of exercise-induced stress. By utilizing data from wearable sensors and real-time location systems, the framework aims to provide a comprehensive understanding of athletes’ physiological responses during training.
📈 Results
The implementation of the IPM-EFPM system has shown promising results in accurately recording fitness strain and stress levels. The use of LSTM and machine learning algorithms has facilitated the discovery of new insights into athletes’ health and performance, paving the way for more effective training regimens.
🌍 Impact and Implications
The findings from this study could significantly impact the sports industry, particularly for basketball players. By providing a more precise understanding of exercise-induced stress, coaches and trainers can tailor training programs to optimize performance and reduce the risk of injury. The potential applications of this technology extend beyond basketball, offering valuable insights for various sports and fitness disciplines.
🔮 Conclusion
This research highlights the transformative potential of artificial intelligence in the realm of sports science. The IPM-EFPM framework not only enhances the accuracy of fitness pressure measurements but also opens new avenues for understanding the complex relationship between physical activity and psychological stress. As AI continues to evolve, its integration into sports training could lead to improved athlete performance and well-being.
💬 Your comments
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Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology.
Abstract
This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. This study employs the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM) to perform automated stress tests that employ AI to enhance the precision of exercise and fitness pressure measurements. Basketball training programs can benefit from this framework’s utilization of state-of-the-art technology, meticulous monitoring of exercise-induced stress, and continuous validation and improvement. The IPM-EFPM system gathers data from wearable sensors, uses real-time location systems, and employs artificial intelligence’s Long Short-Term Memory (LSTM) and machine learning algorithms to uncover new insights in healthcare and sports. To accurately record fitness strain, physical activity, exercise-induced stress, and sports like basketball, this system employs cutting-edge artificial intelligence technologies, such as wearable sensors and current gathering data methods. Placement of sensors, real-time data collecting, data preprocessing and integrating, evaluation of stress by artificial intelligence algorithms, discovery and application of new information, validation and improvement are all parts of an iterative method that has been fine-tuned for use in sports and fitness settings by the IPM-EFPM. Examining the intricate relationship between AI, physical activity, and psychological stress is the main objective of this research. This could have real-world uses tailored to the sports world, particularly for basketball players.
Author: [‘Liu R’, ‘Shen W’]
Journal: SLAS Technol
Citation: Liu R and Shen W. Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology. Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology. 2025; 33:100328. doi: 10.1016/j.slast.2025.100328