Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM

TL;DR

This paper presents a real-time AI-based system using MediaPipe and LSTM for posture monitoring and risk assessment in manual lifting tasks, aiming to reduce musculoskeletal disorders through immediate feedback.

Contribution

It introduces a novel integrated system combining AI-driven posture detection with real-time risk assessment and user feedback for manual lifting safety.

Findings

High accuracy in posture detection

Significant improvement over existing methods in real-time feedback

Effective risk level determination for manual lifting

Abstract

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.