Cloud computing is becoming increasingly widespread due to high volume of data, improved data collection and interorganisational collaborations. It is also making its way to biomedical data applications, and on this blog post we will introduce one of these applications based on an article in the Journal of Building Engineering, called “Development of spatially contextualised and AI-enabled digital twin for asthma-specific indoor air quality management”, written by Negin Khosh-Amadi, Saeed Talebi, Franco Cheung and Mustafa Al-Adhami.
As nowadays people spend most of their time indoors, the air quality in these spaces is becoming increasingly important. It has been shown that pollution, a major respiratory irritant, is 2-5 times stronger indoors than outdoors. The people especially vulnerable to these increased pollutant levels, are asthmatics, who already suffer from a chronic condition affecting their respiratory system. This, together with the advancement of technology and intelligent monitoring systems, motivated the beforementioned authors to develop an AI-enabled digital twin that uses cloud computing, to monitor and indirectly manage indoor air quality.
Which factors influence indoor air quality?
| Factor | Impact |
|---|---|
| PM | Increased asthma symptom frequency, including breathing problems and wheezing |
| CO2 | Indicator of ventilation effectiveness; high levels may indicate poor ventilation, leading to accumulation of pollutants that |
| affect asthma. | |
| Relative humidity | High humidity promotes mould growth, worsening asthma symptoms while low humidity irritates airways |
| Temperature | Mainly affects comfort and may exacerbate respiratory distress under extreme conditions. |
| TVOC | Irritates airways, triggers asthma symptoms |
| CO | Reduces oxygen delivery to the body, aggravates respiratory symptoms, increases risk of exacerbations |
| NO2 | Irritates the airways, reduces lung function, increases asthma exacerbations |
| O3 | Triggers airway inflammation, decreases lung function, provokes asthma attacks |
Proposed solution and cloud usage
- The building is covered with IoT sensors collecting data of the factors influencing indoor air quality
- The collected data is stored in a cloud-based environment such as Azure SQL
- Real-time visualisation of the data is provided using Revit and Power BI
- Linear regression and time-series prediction is used to predict when air quality will go below accepted levels for asthmatics
- The building manager is notified by email, so they could take appropriate measures before the situation arises
Original article
Khosh-Amadi, Negin, et al. “Development of a spatially contextualised and AI-enabled digital twin for asthma-specific indoor air quality management.” Journal of Building Engineering (2025): 115027.