Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety

Area of Science:

• Triboelectric Nanogenerators
• Artificial Intelligence
• Maritime Technology

Background:

• Ship crew monitoring is crucial for safety and efficiency.
• Traditional methods like video surveillance raise privacy concerns.
• Integrating sensors and AI offers a potential solution for data processing.

Purpose of the Study:

• To develop a deep learning-assisted triboelectric smart mat system for crew monitoring.
• To address privacy concerns associated with traditional surveillance methods.
• To enable comprehensive and real-time monitoring of ship personnel and cargo.

Main Methods:

• Fabrication of a minimalist triboelectric smart mat using conductive sponge and fluorinated ethylene propylene membrane.
• Development of a dual-channel measurement method to enhance signal stability.
• Application of deep learning algorithms for analyzing sensory data.

Main Results:

• The system successfully achieved personnel and status identification, positioning, and counting.
• Comprehensive crew and cargo monitoring was realized through real-time sensory data analysis.
• The dual-channel method improved signal stability for reliable data acquisition.

Conclusions:

• The deep learning-assisted triboelectric smart mat system provides a privacy-preserving solution for ship crew monitoring.
• The system enhances the ability to handle emergencies through real-time surveillance.
• It offers an effective method for building ship Internet of Things (IoT) and ensuring personnel safety.