A Human Digital Twin for factory workers to predict fatigue using computer vision

Description:

A cutting-edge solution that utilizes RGB camera feeds to identify human skeletons with models like MediaPipe and OpenPose, analyzing poses and movements to assess fatigue. It uniquely identifies individuals without facial recognition, using limb length and walking patterns, to create and update a digital twin for ongoing, personalized fatigue monitoring.

Key Advantages:

• Non-invasive and privacy-preserving identification using limb length and walking patterns instead of facial recognition.
• Real-time fatigue assessment to enhance workplace safety and productivity.
• Use of a personalized digital twin for accurate and individualized monitoring.
• Application of open-source skeleton detection models for cost-effective implementation.

Problems Solved:

• Reduces the risk of workplace accidents by providing real-time fatigue assessments.
• Eliminates the need for invasive and privacy-intrusive identification methods.
• Improves long-term health outcomes by monitoring and managing worker fatigue.

Market Applications:

• Occupational health and safety in manufacturing and industrial settings.
• Workforce management and productivity enhancement tools.
• Health and wellness monitoring in professional sports and physical training.