AeroSafe: Mobile Indoor Air Purification using Aerosol Residence Time Analysis and Robotic Cough Emulator Testbed

2025-08-04

AI Summary

Portable air purifiers struggle to effectively mitigate exposure to cough-generated aerosols in high-risk settings such as healthcare facilities. Method: This study establishes a closed-loop validation system integrating a robotic cough-simulation platform with a digital twin predictive model. It innovatively combines a mobile anthropomorphic manikin, an autonomously navigating purification robot, and a multimodal sensing module to enable end-to-end experimental validation—from aerosol generation and real-time sensing to dynamic response. A novel digital twin framework is proposed, fusing a physics-based compartmental model with an LSTM–graph convolutional network to predict aerosol concentration evolution and residence time. Results: The framework achieves high prediction accuracy, with mean error in residence time below 35 seconds. Experiments demonstrate that dynamic purification strategies significantly reduce pathogen exposure risk compared to static filter placement, establishing a deployable, intelligent air purification paradigm for respiratory infectious disease control.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Indoor air quality plays an essential role in the safety and well-being of occupants, especially in the context of airborne diseases. This paper introduces AeroSafe, a novel approach aimed at enhancing the efficacy of indoor air purification systems through a robotic cough emulator testbed and a digital-twins-based aerosol residence time analysis. Current portable air filters often overlook the concentrations of respiratory aerosols generated by coughs, posing a risk, particularly in high-exposure environments like healthcare facilities and public spaces. To address this gap, we present a robotic dual-agent physical emulator comprising a maneuverable mannequin simulating cough events and a portable air purifier autonomously responding to aerosols. The generated data from this emulator trains a digital twins model, combining a physics-based compartment model with a machine learning approach, using Long Short-Term Memory (LSTM) networks and graph convolution layers. Experimental results demonstrate the model's ability to predict aerosol concentration dynamics with a mean residence time prediction error within 35 seconds. The proposed system's real-time intervention strategies outperform static air filter placement, showcasing its potential in mitigating airborne pathogen risks.

Problem

• Enhancing indoor air purification efficacy using robotic cough emulation
• Addressing overlooked respiratory aerosol concentrations in portable air filters
• Predicting aerosol dynamics for real-time airborne pathogen risk mitigation

Innovation

• Robotic cough emulator testbed for aerosol analysis
• Digital-twins model with LSTM and graph convolution
• Autonomous air purifier responding to real-time aerosols