IoT-based Industrial Safety Helmet with Worker Fatigue Prediction

A smart industrial helmet designed for hazardous work environments like mining, construction, or manufacturing. This helmet is equipped with pulse, GSR (galvanic skin response), and accelerometer sensors to monitor physiological and physical parameters of the worker. The data is transmitted in real-time to the cloud, where a deep neural network model trained on Google Colab predicts worker fatigue levels. Upon detecting fatigue or stress, the system triggers alerts through buzzers or mobile notifications, ensuring timely intervention and enhanced worker safety.

What You’ll Receive with This Project Kit:

All Essential Project Components:
Includes industrial-grade sensors for pulse, GSR, and motion; a microcontroller (ESP32); helmet integration materials; and cloud interfacing modules.

Fully Functional Source Code:
Ready-to-deploy, cleanly documented code for sensor data acquisition, cloud upload, and fatigue prediction logic using ML inference. Compatible with Google Colab-trained deep learning models.

Step-by-Step Working Methods:
A user-friendly assembly and configuration manual with detailed sensor wiring, helmet fitting guide, cloud setup walkthrough, ML model deployment, and testing procedures.

100-Page Detailed Project Report (PDF):
Ready-to-submit technical report covering problem definition, literature review, system architecture, neural network design, result validation, screenshots, and future enhancements. Ideal for final-year evaluations and project reviews.

Advanced Mobile App Interface:
Comes with a custom-built mobile/web app dashboard for real-time monitoring, alert management, and fatigue level visualization—offering a professional touch beyond basic platforms.