Volatile organic compounds recognition using a smartphone camera and fluorometric sensors - Ahn, Jungmo; Kim, Hyungi; Ko, Jeong Gil; Kim, Eunha (UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers, 2018)
Volatile organic compound (VOC) recognition systems can be helpful tools in monitoring today's living environments surrounded by harmful chemicals including dangerous VOCs. By designing a mobile system where users can easily detect VOC materials in their surroundings, people can avoid VOC-contained environments or take actions to improve their living conditions. Unfortunately, current VOC detection systems require bulky devices, and the current technology does not allow this detection and classification process to take place in real-time near the user. In this work, we introduce a novel VOC recognition process using a smartphone camera and paper-based fluorometric sensors. Fluorometric sensors will change their color patterns as they are exposed to different VOC materials and the smartphone camera combined with simple machine learning algorithms can be used to classify different VOC materials. Specifically, we introduce how a fluorometric sensor dataset of different VOC materials is gathered, and present a set of preliminary machine learning algorithms for VOC classification using smartphones. Our results show up to ∼88% accuracy in classifying eight different types of VOC materials using an LDA model.
Keywords: Classification process; Current technology; Living conditions; Living environment; Recognition process; Recognition systems; Smart-phone cameras; Volatile organic compound (VOC); Fluorometric sensor; Image classification; Machine learning; Volatile organic compounds