Learning-Based Odor Anomaly Detection Using Bosch BME688 in Indoor Environments

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201198" target="_blank" >RIV/00216305:26220/26:0201198 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268723" target="_blank" >http://dx.doi.org/10.1109/ICUMT67815.2025.11268723</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268723" target="_blank" >10.1109/ICUMT67815.2025.11268723</a>

Result language
angličtina
Original language name
Learning-Based Odor Anomaly Detection Using Bosch BME688 in Indoor Environments
Original language description
This paper presents a complete pipeline for odorbased anomaly detection using the Bosch BME688 metal-oxide gas sensor in indoor environments. Measurements were conducted across several classes of everyday and chemically aggressive odors, including Normal Air, Coffee, Vinegar, Acetone, Bleach, Biowaste, and Paint Thinner. The acquired resistance data were preprocessed, normalized, and analyzed using dimensionality reduction techniques (UMAP, PCA) to explore data structure and class separability.Three anomaly detection models were implemented and compared: a Long Short-Term Memory (LSTM) autoencoder, a dense (fully connected) autoencoder, and the Isolation Forest algorithm. Results show that while the dense autoencoder achieved the highest classification accuracy, particularly on borderline classes, the LSTM autoencoder provided better anomaly separation for well-defined anomaly classes. The findings demonstrate that deep learning models based on resistance signals from the BME688 can effectively detect odor anomalies.
Czech name
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Czech description
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Project
<a href="/en/project/FW07010015" target="_blank" >FW07010015: Indoor environment quality monitoring of buildings using odor sensors and artificial intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3315-7675-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
176-181
Publisher name
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Place of publication
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Event location
Florencie, Itálie
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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