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

TL;DR

AeroSafe introduces a robotic cough emulator and digital twins to improve indoor air purification by accurately modeling aerosol dynamics and optimizing filter placement, especially in high-risk environments.

Contribution

The paper presents a novel robotic emulator and a hybrid physics-ML digital twins model for aerosol residence time analysis, enhancing indoor air purification strategies.

Findings

Model predicts aerosol residence time with 35 seconds error

Real-time intervention outperforms static filter placement

System effectively reduces airborne pathogen risk

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.