We are excited to share a new, outstanding achievement accomplished by University Of Patras, within the context of TwinAir project!
The article “Unobtrusive Human Activity Recognition Using Multivariate Indoor Air Quality Sensing and Hierarchical Event Detection” has been published at “Sensors” Journal, by MDPI.
The authors of the article are: Grigoriοs Protopsaltis,Christos Mountzouris, Gerasimos Theodorou and John Gialelis and its respective DOI is the following: https://doi.org/10.3390/s26092857
The abstract and the keywords of the publication can be found below:
Abstract
Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management.
Keywords:
indoor air quality; activity recognition; hierarchical classification; environmental monitoring
You can reach the full article following this link: https://www.mdpi.com/1424-8220/26/9/2857