Investigation and Classification of Unpleasant Odor in Restroom Using Intelligent Signal Processing Technique

Abstract

Restrooms are essential facilities for urination and defecation, and maintaining hygiene and user comfort in these spaces is crucial. Despite similar structural designs, restrooms often exhibit varying odors, making odor detection and classification important for effective maintenance. The primary challenge is the effective detection and classification of various restroom odors to maintain hygiene and enhance user comfort. Traditional methods may not provide the accuracy or efficiency required for real-time applications. This study aims to develop a reliable method for differentiating between various restroom odors using an Electronic Nose (E-Nose) system integrated with Case-Based Reasoning (CBR) analysis. The proposed system employs the E-Anfun 1.0 model of the E-Nose to capture odor profiles. These profiles are then analyzed using a CBR framework to accurately differentiate and classify the odors. The system achieved a classification accuracy rate of 100%, demonstrating the effectiveness of integrating the E-Anfun 1.0 E-Nose with CBR for odor detection. The significant findings include the system's ability to accurately classify different odor profiles, which can be utilized to enhance restroom maintenance practices. In conclusion, this method offers a promising approach to improving the hygiene and comfort of public restrooms by providing accurate and efficient odor classification. The implementation of such a system can lead to better maintenance practices and an overall improvement in restroom hygiene standards.