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

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

Stať ve sborníku v databázi WoS či Scopus

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.

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.

odor anomaly detection, BME688, smell recognition, e-nose implementation, machine learning, autoencoder

odor anomaly detection, BME688, smell recognition, e-nose implementation, machine learning, autoencoder

KŘÍŽ, P.; SIKORA, P.; ŘÍHA, K.; BURGET, R.; AVRAMOVIĆ, N.

2026

03.11.2025

979-8-3315-7675-2

2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

176

181

6