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Volatile organic compounds recognition using a smartphone camera and fluorometric sensors
  • Ahn, Jungmo ;
  • Kim, Hyungi ;
  • Ko, Jeong Gil ;
  • Kim, Eunha
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Publication Year
2018-10-08
Journal
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
Publisher
Association for Computing Machinery, Inc
Citation
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, pp.1364-1371
Keyword
Fluorometric sensorImage classificationMachine learningVolatile organic compounds
Mesh Keyword
Classification processCurrent technologyLiving conditionsLiving environmentRecognition processRecognition systemsSmart-phone camerasVolatile organic compound (VOC)
All Science Classification Codes (ASJC)
SoftwareHuman-Computer InteractionInformation Systems
Abstract
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36327
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85058315939&origin=inward
DOI
https://doi.org/10.1145/3267305.3274185
Journal URL
http://dl.acm.org/citation.cfm?id=3267305
Type
Conference
Funding
This work was supported by the Ajou University research fund, the National Research Foundation of Korea funded by the Ministry of Science and ICT (2018R1C1B6003869), and the DGIST R&D program funded by MSIT (CPS Global Center).
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Kim, Eun ha김은하
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