Integrating Machine Learning for Intelligent Fitness Exercise Monitoring
Аннотация
Fitness is important in people’s lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. People obtain their fitness knowledge mostly from social media.
Research indicates that maintaining fitness is crucial for promoting a healthy way of living and is used to assess one's health-related quality of life. While engaging a fitness trainer can be an effective approach to encourage regular exercise and overall well-being, it may not always be feasible or affordable in certain situations. It is worth noting that exercise has numerous health benefits, but if performed incorrectly, it can be both ineffective and potentially hazardous. Individuals who work out without proper supervision often make mistakes such as using improper forms, which can lead to severe consequences, such as hamstring injuries or falls.
but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people’s fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a human pose estimation algorithms such as Yolov7, OpenPose and particularly Mediapipe, to optimize squat
performance across various skill levels, this system provides real-time analysis of squat techniques. Customized modes tailored for beginners and professionals deliver personalized feedback, empowering users to refine their form effectively. By employing techniques from computer vision and machine learning, including MediaPipe, OpenCV, and Python, the system tracks users' movements, providing on-screen guidance and auditory cues for posture correction and workout progression. AI-Fit offers a solution for individuals to exercise safely with expert guidance and addresses the need for personalized fitness training, injury prevention, and motivation, ultimately enhancing users' overall physical fitness and well-being.
Keywords: Computer Vision,OpenCV,Yolov7,MediaPipe,BlazePose,COCO,VGG,MPII,Image processing, Human pose estimation, Pose tracking algorithms, Real-time movement analysis, Posture correction
Research indicates that maintaining fitness is crucial for promoting a healthy way of living and is used to assess one's health-related quality of life. While engaging a fitness trainer can be an effective approach to encourage regular exercise and overall well-being, it may not always be feasible or affordable in certain situations. It is worth noting that exercise has numerous health benefits, but if performed incorrectly, it can be both ineffective and potentially hazardous. Individuals who work out without proper supervision often make mistakes such as using improper forms, which can lead to severe consequences, such as hamstring injuries or falls.
but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people’s fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a human pose estimation algorithms such as Yolov7, OpenPose and particularly Mediapipe, to optimize squat
performance across various skill levels, this system provides real-time analysis of squat techniques. Customized modes tailored for beginners and professionals deliver personalized feedback, empowering users to refine their form effectively. By employing techniques from computer vision and machine learning, including MediaPipe, OpenCV, and Python, the system tracks users' movements, providing on-screen guidance and auditory cues for posture correction and workout progression. AI-Fit offers a solution for individuals to exercise safely with expert guidance and addresses the need for personalized fitness training, injury prevention, and motivation, ultimately enhancing users' overall physical fitness and well-being.
Keywords: Computer Vision,OpenCV,Yolov7,MediaPipe,BlazePose,COCO,VGG,MPII,Image processing, Human pose estimation, Pose tracking algorithms, Real-time movement analysis, Posture correction