Indoor air quality platform alerts users with smart localized warnings
Large Language Model based air quality monitoring and localized alert generation
Distributed, Parallel, and Cluster ComputingComputers and Society
Summary
Indoor air pollution can cause various health problems, often worse than outdoor pollution, but most devices only monitor passively. The authors designed a system called EnQyMo that uses sensors and Bluetooth beacons to track where people are exposed to bad air indoors. It sends alerts only to those in the affected areas. The system uses large language models to understand health regulations and scientific information automatically to decide when air pollution is dangerous.
What this means in practice
- •For workplace safety teams: Detect unhealthy indoor air zones and alert only employees currently present, enabling targeted health risk warnings in large office spaces.
- •For smart building operators: Integrate sensor data with user location information to automate real-time air quality risk notifications tailored to specific indoor areas.
Authors
Ricardo Vieira, Luis Tavares, Kaylane Lima, Lucas De Souza, Arthur Poggy, João Lima, Vitor Pinheiro, Markus Endler
Abstract
Poor indoor air quality can cause up to five times more direct health problems to occupants than outdoor air. In particular, it may cause headaches, fatigue, eye/throat irritation, and long-time exposure is linked to respiratory and heart as well as some forms of cancer. Despite the importance of indoor health and well-being, most current monitoring devices and systems (usually for offices and workspaces) are passive. The Environmental Quality Monitor (EnQyMo) platform is a generic Internet of Things (IoT) middleware designed to process several sensor data related to air quality in indoor spaces and correlate this data with health exposure risks of users/workplace employees. Using Bluetooth Low Energy (BLE) beacons and a mobile IoT middleware it is able to identify the (smartphone) users exposed to these polluted air or high CO2 (carbon dioxide) levels, and generate location-specific alarms only to the users at the places with the unhealthy air conditions. At the core of EnQyMo is an agency of Large Language Models (LLMs) capable of interpreting regulatory standards and scientific literature to automatically identify critical health exposure levels.