Innovative Real-Time Indoor Tracking System Enhances Disease Spread Detection
Concordia researchers have developed a real-time tracking system using cameras and sensors to detect and contain disease spread indoors, promising faster outbreak management and improved indoor health safety.
Researchers from Concordia University have developed an advanced real-time tracking technology that leverages camera and sensor data to monitor the movement of infected individuals and the dispersion of pathogens within indoor environments. This groundbreaking method aims to accelerate the identification of areas at risk for disease transmission, potentially transforming how outbreak management is conducted in facilities such as hospitals, offices, and public spaces.
The system combines real-time visual and sensor-based tracking with sophisticated algorithms that model the dispersal of airborne pathogens, allowing for accurate assessments of infection risk levels in specific indoor zones. Notably, it can dynamically alert ventilation systems to optimize airflow, thereby reducing the likelihood of pathogen spread. According to Ph.D. candidate Zeinab Deldoost, this approach significantly cuts down simulation times compared to traditional models, providing near-instantaneous risk estimations.
A key innovation of the model is its simplified airflow calculation technique. Instead of complex estimations based on individual movements, the model treats individuals as moving emission sources, which simplifies calculations and enhances speed. Validation shows that a person's physical presence only briefly disrupts airflow, which typically returns to normal within 40 seconds and affects areas only about one meter around their path. This allows the system to perform quick dispersion simulations — approximately 3.8 seconds for a one-second dispersion pattern on a standard laptop — making it particularly suitable for large, dynamic buildings.
Fuzhan Nasiri, another researcher involved, emphasizes that this technology could provide decision-makers with real-time data, improving intervention responses and containment strategies. The potential of integrating AI training with extensive scenario data could further refine predictive capabilities and facilitate widespread implementation, ultimately enhancing indoor health safety protocols.
Published in 'Building and Environment' (2025), this innovative system represents a significant step toward smarter, more responsive indoor health management, especially crucial amid ongoing concerns about airborne infectious diseases. (Source: https://medicalxpress.com/news/2025-08-real-camera-sensor-based-tracking.html)
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