​Worker Identification
ID Number Recognition: Use Optical Character Recognition (OCR) or object detection models to detect and read ID numbers from uniforms or badges. 

Activity Recognition
Pose Estimation: Use pose estimation models to track body movements and keypoints (e.g., hands, arms, legs).

Action Classification
Train a machine learning model to classify specific activities based on body movements:

• Mold Cleaning: Detect repetitive arm movements and body posture.
• Bolt Tightening: Recognize hand and arm motions associated with using tools.
  
• Labeling: Identify fine motor skills and hand movements.
  
• Mold Opening: Detect lifting or pulling motions.​

This system can significantly improve factory management by automating productivity tracking and ensuring efficiency. ​

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Artificial Intelligence

Human Behaviour

Key Component

Overview

Creating an AI system for productivity detection in a factory environment involves similar principles to the prison scenario but with a focus on factory workers and their tasks. Below is a detailed outline of how to design and implement such a system. ​The AI system will:

• Use cameras to monitor factory workers.
• Recognize workers via ID numbers (e.g., from uniforms, badges).
  
• Analyze body movements to determine specific activities (e.g., mold cleaning, bolt tightening, labeling, mold opening, etc.).
  
• Label and log activities for productivity tracking and efficiency analysis.