Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917756217065472 |
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| author | Bagga, Ereena Yang, Ang |
| author_facet | Bagga, Ereena Yang, Ang |
| contents | This research focuses on developing a real-time posture monitoring and risk assessment system for manual lifting tasks using advanced AI and computer vision technologies. Musculoskeletal disorders (MSDs) are a significant concern for workers involved in manual lifting, and traditional methods for posture correction are often inadequate due to delayed feedback and lack of personalized assessment. Our proposed solution integrates AI-driven posture detection, detailed keypoint analysis, risk level determination, and real-time feedback delivered through a user-friendly web interface. The system aims to improve posture, reduce the risk of MSDs, and enhance user engagement. The research involves comprehensive data collection, model training, and iterative development to ensure high accuracy and user satisfaction. The solution's effectiveness is evaluated against existing methodologies, demonstrating significant improvements in real-time feedback and risk assessment. This study contributes to the field by offering a novel approach to posture correction that addresses existing gaps and provides practical, immediate benefits to users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12796 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM Bagga, Ereena Yang, Ang Artificial Intelligence Computer Vision and Pattern Recognition This research focuses on developing a real-time posture monitoring and risk assessment system for manual lifting tasks using advanced AI and computer vision technologies. Musculoskeletal disorders (MSDs) are a significant concern for workers involved in manual lifting, and traditional methods for posture correction are often inadequate due to delayed feedback and lack of personalized assessment. Our proposed solution integrates AI-driven posture detection, detailed keypoint analysis, risk level determination, and real-time feedback delivered through a user-friendly web interface. The system aims to improve posture, reduce the risk of MSDs, and enhance user engagement. The research involves comprehensive data collection, model training, and iterative development to ensure high accuracy and user satisfaction. The solution's effectiveness is evaluated against existing methodologies, demonstrating significant improvements in real-time feedback and risk assessment. This study contributes to the field by offering a novel approach to posture correction that addresses existing gaps and provides practical, immediate benefits to users. |
| title | Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12796 |