Shah Alam, Malaysia — In the wake of the COVID-19 pandemic, researchers from Management & Science University, University of Baghdad, and other global institutions have developed a cutting-edge smart helmet system integrated with IoT and thermal imaging technology, designed to detect potential COVID-19 carriers in real time—without physical contact.
The study, published in the International Journal of Psychosocial Rehabilitation in March 2020, Volume 24, Issue 7, presents an innovative solution to one of the most pressing issues faced by healthcare systems worldwide: the safe, fast, and efficient screening of large populations during outbreaks.
Reimagining Health Screening in a Pandemic Era
At the height of the pandemic, traditional thermal screening using handheld infrared thermometers proved inadequate—slow, labor-intensive, and risk-prone. Frontline health workers faced exposure while manually checking temperatures in long queues, especially in airports, malls, and transportation hubs.
To combat this challenge, the team led by Dr. Mohammed N. Abdulrazaq, currently working in Gulf University, Bahrain, conceptualized and built a smart helmet, combining:
This multi-sensor helmet automatically scans crowds, detects elevated body temperatures, and sends alerts—along with facial images and location coordinates—to healthcare authorities for prompt response.
Inside the Smart Helmet: How It Works
The system architecture consists of three interconnected subsystems:
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Input Layer – Thermal and optical cameras mounted on the helmet scan individuals for high temperatures and capture facial images.
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Processing Layer – An Arduino-based microcontroller processes the image and temperature data, integrating face recognition using machine learning algorithms (Viola–Jones Cascade Classifier).
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Output Layer – If a high temperature is detected, the system triggers a notification to health officers via mobile app, along with location and image data.
Additionally, Google Location History (GLH) is integrated to trace recent movements of suspected cases—an intelligent add-on to assist with contact tracing and epidemiological mapping.
Simulation, Results & Reliability
Before field deployment, the smart helmet was rigorously simulated using Proteus software, ensuring reliability in detecting temperature anomalies. It was further tested through a live prototype powered by NodeMCU, displaying data in real time via the Blynk IoT platform.
According to the research, this solution not only speeds up the screening process but also reduces direct human interaction—minimizing the risk of transmission and enhancing frontline safety.
Relevance Beyond COVID-19
The researchers emphasize that the smart helmet holds long-term potential beyond the coronavirus. With adaptability, it can be used for future pandemics or integrated into smart city infrastructure for public health monitoring and disaster preparedness.
Dr. Mohammed and the international team believe the integration of AI, remote sensing, and IoT into personal protective equipment represents a transformative leap in public health technology.
“Early detection with minimal human involvement is the key to controlling pandemics. Our smart helmet provides an efficient, non-invasive solution that’s scalable, accurate, and smart,” said Dr. Mohammed.