Intelligent Platform Based on Smart PPE for Safety in Workplaces

Area of Science:

• Occupational Health and Safety
• Artificial Intelligence
• Wearable Technology

Background:

• Workers spend a significant portion of their lives at work, necessitating enhanced safety measures.
• Traditional workplace equipment requires adaptation to integrate new technologies for improved safety and connectivity.
• Smart Personal Protective Equipment (PPE) and wearable technologies offer potential for real-time data extraction to mitigate workplace risks.

Purpose of the Study:

• To propose an architecture for smart PPE that enhances worker safety using AI and edge computing.
• To develop a system capable of early prediction and notification of environmental anomalies in the workplace.
• To improve the overall safety and reduce occupational illnesses and accidents.

Main Methods:

• An architecture utilizing three smart PPE devices: helmet, bracelet, and belt.
• Processing collected worker and environmental data using artificial intelligence (AI) techniques via edge computing.
• Employing a combination of models including convolutional neural networks, long short-term memory, and Gaussian Models, with a support vector machine for final decision-making.

Main Results:

• The proposed system effectively processes information from smart PPE using AI and edge computing.
• An ensemble of AI models was utilized, with a support vector machine weighting their outputs.
• The system achieved an area under the curve (AUC) of 0.81 in anomaly detection.

Conclusions:

• The developed smart PPE architecture effectively enhances worker safety through early anomaly detection.
• AI and edge computing integrated into wearable devices provide a robust solution for improving occupational health.
• The system demonstrates significant potential in reducing workplace accidents and occupational illnesses.