Summary of Real-time Posture Monitoring and Risk Assessment For Manual Lifting Tasks Using Mediapipe and Lstm, by Ereena Bagga and Ang Yang
Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM
by Ereena Bagga, Ang Yang
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A proposed AI-driven posture monitoring system aims to reduce the risk of musculoskeletal disorders (MSDs) for workers involved in manual lifting tasks. The system integrates AI-powered posture detection, detailed keypoint analysis, risk level determination, and real-time feedback through a user-friendly web interface. 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI system helps workers lift safely by tracking their posture and giving them instant feedback. Musculoskeletal disorders are a big problem for people who do manual lifting all day. Current methods for improving posture don’t work well because they’re too slow or not personalized enough. This system uses computer vision and artificial intelligence to detect the user’s posture, analyze it in detail, determine their risk level, and provide feedback through a simple website. The goal is to help workers improve their posture, reduce their risk of getting hurt, and make the experience more engaging. |
Keywords
» Artificial intelligence » Tracking
