Skip to main content
SpringerLink
Log in

uSmell: exploring the potential for gas sensors to classify odors in ubicomp applications relative to airflow and distance

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Previous research has shown that gas sensors can be used to classify odors when used in highly controlled experimental testing chambers. However, potential ubicomp applications require these sensors to perform an analysis in less controlled environments, particularly at a distance. In this paper, we discuss our design of uSmell—a gas sensor system for sensing smell in ubicomp environments—and an evaluation of its basic efficacy, effects of airflow and distance on classification accuracy, and in an example application. Our system samples an odor fingerprint from eight metal oxide semiconductor (MOS) gas sensors every second. It then processes the time series data to extract three features that highlight how time and distance affect the eight MOS gas sensors’ ability to react to the gas molecules released by an odor every 5 s; this generates 24 features in total that are then used to train a decision tree classifier. Using this approach, our system can classify a set of odors with 88 % accuracy when placed both in a small container with the samples and in open air 0.5–2 m from the odor samples. We also demonstrate its ability to classify odors in less controlled environments that might be targets for ubicomp applications by deploying it in a bathroom for a week. These results show the potential for applying this sensing toward the development of context-aware systems, such as lifelogging applications or those geared toward enhancing the sustainability of natural resources (e.g., an automatic dual-flush toilet that always uses an appropriate amount of water based on the user’s toileting activities).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abe H, Yoshimura T, Kanaya S, Takahashi Y, Miyashita Y, Sasaki S-I (1987) Automated odor-sensing system based on plural semiconductor gas sensors and computerized pattern recognition techniques. Anal Chim Acta 194:1–9. doi:10.1016/s0003-2670(00)84755-8

    Article  Google Scholar 

  2. Aishima T (1991) Aroma discrimination by pattern recognition analysis of responses from semiconductor gas sensor array. J Agric Food Chem 39:752–756. doi:10.1021/jf00004a027

    Article  Google Scholar 

  3. Aishima T (1991) Discrimination of liquor aromas by pattern recognition analysis of responses from a gas sensor array. Anal Chim Acta 243:293–300. doi:10.1016/s0003-2670(00)82573-8

    Article  Google Scholar 

  4. Alocilja EC, Ritchie NL, Grooms DL (2003) Protocol development using an electronic nose for differentiating E. coli strains. Sensors J IEEE 3:801–805

    Article  Google Scholar 

  5. Arduino (2013) Arduino website. In: Arduino. http://www.arduino.cc/. Accessed 3 Sep 2013

  6. Arroyo E, Bonanni L, Selker T (2005) Waterbot: exploring feedback and persuasive techniques at the sink. ACM 1055059:631–639

    Google Scholar 

  7. Ballantine DS, Rose SL, Grate JW, Wohltjen H (1986) Correlation of surface acoustic wave device coating responses with solubility properties and chemical structure using pattern recognition. Anal Chem 58:3058–3066. doi:10.1021/ac00127a035

    Article  Google Scholar 

  8. Barrett G (2004) Water conservation: the role of price and regulation in residential water consumption. Econ Pap J Appl Econ Policy 23:271–285. doi:10.1111/j.1759-3441.2004.tb00371.x

    Article  Google Scholar 

  9. Beckmann C, Consolvo S, LaMarca A (2004) Some assembly required: supporting end-user sensor installation in domestic ubiquitous computing environments. In: Davies N, Mynatt E, Siio I (eds) Proceedings of the 6th international conference on Ubiquitous computing (Ubicomp ’04), Lecture notes in computer science, vol 3205. Springer, Heidelberg, pp 107–124

  10. Bonanni L, Arroyo E, Lee C-H, Selker T (2005) Exploring feedback and persuasive techniques at the sink. Interactions 12:25–28. doi:10.1145/1070960.1070980

    Article  Google Scholar 

  11. Borazio M, Van Laerhoven K (2012) Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies. In: proceedings of the. 2nd ACM sight international health informatics symposium. ACM, New York, NY, USA, pp 71–80

  12. Carrasco A, Saby C, Bernadet P (1998) Discrimination of Yves Saint Laurent perfumes by an electronic nose. Flavour Fragr J 13:335–348. doi:10.1002/(sici)1099-1026(1998090)13:5<335:aid-ffj753>3.0.co;2-f

    Article  Google Scholar 

  13. Chen S-L, Lee H-Y, Chen C-A, Lin C-C, Luo C-H (2007) A wireless body sensor network system for healthcare monitoring application. In: IEEE Biomedical Circuits Systems Conference 2007 BIOCAS 2007, pp 243–246

  14. Choe EK, Consolvo S, Jung J, Harrison B, Patel SN, Kientz JA (2012) Investigating receptiveness to sensing and inference in the home using sensor proxies. In: proc. 2012 ACM Conf. Ubiquitous Comput. ACM, New York, NY, USA, pp 61–70

  15. Dubowsky S, Genot F, Godding S, Kozono H, Skwersky A, Yu H, Yu LS (2000) PAMM: a robotic aid to the elderly for mobility assistance and monitoring: a ldquo;helping-hand rdquo; for the elderly. In proceedings of the IEEE international conference on robotics and automation ICRA 00, vol 1, pp 570–576

  16. Dutta R, Morgan D, Baker N, Gardner JW, Hines EL (2005) Identification of Staphylococcus aureus infections in hospital environment: electronic nose based approach. Sensors Actuators B Chem 109:355–362. doi:10.1016/j.snb.2005.01.013

    Article  Google Scholar 

  17. Ehrmann S, Jüngst J, Goschnick J (2000) Automated cooking and frying control using a gas sensor microarray. Sensors Actuators B Chem 66:43–45. doi:10.1016/S0925-4005(99)00354-8

    Article  Google Scholar 

  18. EPA (2011) EPA water conservation information page. http://www.epa.gov/oaintrnt/water/index.htm

  19. Francesco FD, Lazzerini B, Marcelloni F, Pioggia G (2001) An electronic nose for odour annoyance assessment. Atmos Environ 35:1225–1234. doi:10.1016/s1352-2310(00)00392-7

    Article  Google Scholar 

  20. Galdikas A, Mironas A, Šetkus A, Zelenin D (2000) Response time based output of metal oxide gas sensors applied to evaluation of meat freshness with neural signal analysis. Sensors Actuators B Chem 69:258–265. doi:10.1016/s0925-4005(00)00505-0

    Article  Google Scholar 

  21. Gardner JW (1991) Detection of vapours and odours from a multisensor array using pattern recognition Part 1: principal component and cluster analysis. Sensors Actuators B Chem 4:109–115. doi:10.1016/0925-4005(91)80185-M

    Article  Google Scholar 

  22. Gardner JW, Bartlett PN (2000) Electronic noses: principles and applications. Meas Sci Technol 11:1087

    Google Scholar 

  23. Gardner JW, Bartlett PN (1999) Electronic noses: principles and applications. Oxford University Press, Oxford

    Google Scholar 

  24. Gendron KB, Hockstein NG, Thaler ER, Vachani A, Hanson CW (2007) In vitro discrimination of tumor cell lines with an electronic nose. Otolaryngol Head Neck Surg 137:269–273. doi:10.1016/j.otohns.2007.02.005

    Article  Google Scholar 

  25. Gil Y, Wu W, Lee J (2012) A synchronous multi-body sensor platform in a Wireless Body Sensor Network: design and implementation. Sensors 12:10381–10394. doi:10.3390/s120810381

    Article  Google Scholar 

  26. Goschnick J, Koerber R (2002) Condition monitoring for intelligent household appliances. Sensors Househ Appl 5:52–68

    Google Scholar 

  27. Harrison B, Consolvo S, Choudhury T (2010) Using multi-modal sensing for human activity modeling in the real world. In: Nakashima H, Aghajan H, Augusto J (eds) Handb Ambient Intell. Smart Environ. Springer, USA, pp 463–478

    Chapter  Google Scholar 

  28. Hirobayashi S, Kimura H, Oyabu T (1999) Detection of human activities by inverse filtration of gas sensor response. Sensors Actuators B Chem 56:144–150. doi:10.1016/s0925-4005(99)00184-7

    Article  Google Scholar 

  29. Hnat TW, Griffiths E, Dawson R, Whitehouse K (2012) Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors. In: proceedings of the 10th ACM Conference Embedded Network Sensor System ACM, New York, NY, USA, pp 309–322

  30. Kappel K, Grechenig T (2009) Show-me: water consumption at a glance to promote water conservation in the shower. ACM 1541984:1–6

    Google Scholar 

  31. Kidd CD, Orr R, Abowd GD, Atkeson CG, Essa IA, MacIntyre B, Mynatt E, Starner TE, Newstetter W (1999) The aware home: a living laboratory for ubiquitous computing research. In: Streitz NA, Siegel J, Hartkopf V, Konomi S (eds) Coop. Build. Integrating Inf. Organ. Arch, Springer, pp 191–198

    Google Scholar 

  32. Kim S, Paulos E (2010) In air: sharing indoor air quality measurements and visualizations. ACM 1753605:1861–1870

    Google Scholar 

  33. Kim Y, Schmid T, Charbiwala ZM, Friedman J, Srivastava MB (2008) NAWMS: nonintrusive autonomous water monitoring system. In: proceedings of the 6th ACM Conference Embedded Network Sensor System ACM, New York, NY, USA, pp 309–322

  34. Kinkeldei T, Zysset C, Münzenrieder N, Tröster G (2012) An electronic nose on flexible substrates integrated into a smart textile. Sens Actuators B Chem 174:81–86. doi:10.1016/j.snb.2012.08.023

    Article  Google Scholar 

  35. Kobayashi Y, Terada T, Tsukamoto M (2011) A context aware system based on scent. In: proceedings of the 15th Annu. Int. Symp. Wearable Comput. ISWC, pp 47–50

  36. Labreche S, Bazzo S, Cade S, Chanie E (2005) Shelf life determination by electronic nose: application to milk. Sens Actuators B Chem 106:199–206. doi:10.1016/j.snb.2004.06.027

    Article  Google Scholar 

  37. Larson E, Froehlich J, Campbell T, Haggerty C, Atlas L, Fogarty J, Patel SN (2012) Disaggregated water sensing from a single, pressure-based sensor: an extended analysis of HydroSense using staged experiments. Pervasive Mob Comput 8:82–102. doi:10.1016/j.pmcj.2010.08.008

    Article  Google Scholar 

  38. Lester J, Tan D, Patel S, Brush AJB (2010) Automatic classification of daily fluid intake. In: pervasive comput technol healthc pervasive health 2010 4th Int. Conf. -NO Permis, pp 1–8

  39. Loutfi A, Broxvall M, Coradeschi S, Karlsson L (2005) Object recognition: a new application for smelling robots. Robot Auton Syst 52:272–289. doi:10.1016/j.robot.2005.06.002

    Article  Google Scholar 

  40. Mann S (2003) Intelligent bathroom fixtures and systems: EXISTech corporation’s safebath project. Leonardo 36:207–210. doi:10.1162/002409403321921424

    Article  Google Scholar 

  41. Marques L, De Almeida AT (2000) Electronic nose-based odour source localization. In: Proceedings of the 6th international workshop on advanced motion control. IEEE, pp 36–40

  42. Matsuura S (1993) New developments and applications of gas sensors in Japan. Sens Actuators B Chem 13:7–11. doi:10.1016/0925-4005(93)85311-w

    Article  Google Scholar 

  43. Moriizumi T, Nakamoto T, Sakuraba Y (1992) Pattern recognition in electronic noses by Artificial Neural Network models. Sens Sens Syst Electron Nose E212:217–236

    Article  Google Scholar 

  44. Di Natale C, Davide FAM, D’Amico A, Nelli P, Groppelli S, Sberveglieri G (1996) An electronic nose for the recognition of the vineyard of a red wine. Sensors Actuators B Chem 33:83–88. doi:10.1016/0925-4005(96)01918-1

    Article  Google Scholar 

  45. Paolesse R, Alimelli A, Martinelli E, Natale CD, D’Amico A, D’Egidio MG, Aureli G, Ricelli A, Fanelli C (2006) Detection of fungal contamination of cereal grain samples by an electronic nose. Sensors Actuators B Chem 119:425–430. doi:10.1016/j.snb.2005.12.047

    Article  Google Scholar 

  46. Pathange LP, Mallikarjunan P, Marini RP, O’Keefe S, Vaughan D (2006) Non-destructive evaluation of apple maturity using an electronic nose system. J Food Eng 77:1018–1023. doi:10.1016/j.jfoodeng.2005.08.034

    Article  Google Scholar 

  47. Perera A, Pardo T, Sundi T, Gutierrez-Osuna R, Marco S, Nicolas J (2001) IpNose: electronic nose for remote bad odour monitoring system in landfill sites. In: Proceedings of the 8th conference Eurodeur. Paris, France, pp 19–21

  48. Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299:352–355. doi:10.1038/299352a0

    Article  Google Scholar 

  49. Pornpanomchai C, Jurangboon K, Jantarasee K (2010) Instant coffee classification by electronic noses. In: Proceedings of 2010 2nd International Conference on Mechanical and Electronics Engineering (ICMEE), 1–3 Aug 2010, vol 1, pp V1-10–V1-13

  50. Pornpanomchai C, Suthamsmai N (2008) Beer classification by electronic nose. In: Proceedings of the 2008 international conference on wavelet analysis and pattern recognition, 30–31 Aug 2008, vol 1, pp 333–338

  51. Ramalho O (2000) Correspondences between olfactometry, analytical and electronic nose data for 10 indoor paints. Analusis 28:207–215. doi:10.1051/analusis:2000280207

    Article  Google Scholar 

  52. Roberts PJW and W (2002) Turbulent Diffusion. In: H. Shen AC (ed) Environ. Fluid Mech.—Theor. Appl. ASCE Press, Reston, Virginia, pp 7–45

  53. Seong Y, Narumi T, Akagawa T (2008) Automatic data extracting software for retrieval of lifetime photos using scent information. ACM SIGGRAPH ASIA 2008 Posters

  54. Shaham O, Carmel L, Harel D (2005) On mappings between electronic noses. Sensors Actuators B Chem 106:76–82. doi:10.1016/j.snb.2004.05.039

    Article  Google Scholar 

  55. Starner T, Auxier J, Ashbrook D, Gandy M (2000) The gesture pendant: a self-illuminating, wearable, infrared computer vision system for home automation control and medical monitoring. In: Fourth Int. Symp. Wearable Comput, pp 87–94

  56. Strengers YAA (2011) Designing eco-feedback systems for everyday life. ACM 1979252:2135–2144

    Google Scholar 

  57. Tanabe T (1982) Cooking utensil controlled by gas sensor output and thermistor output. US Patent 4,316,068, 16 Feb 1982

  58. Tikk K, Haugen J-E, Andersen HJ, Aaslyng MD (2008) Monitoring of warmed-over flavour in pork using the electronic nose—correlation to sensory attributes and secondary lipid oxidation products. Meat Sci 80:1254–1263. doi:10.1016/j.meatsci.2008.05.040

    Article  Google Scholar 

  59. Varshney U (2007) Pervasive healthcare and wireless health monitoring. Mob Netw Appl 12:113–127. doi:10.1007/s11036-007-0017-1

    Article  Google Scholar 

  60. Wilson AD, Baietto M (2009) Applications and advances in electronic-nose technologies. Sensors 9:5099–5148. doi:10.3390/s90705099

    Article  Google Scholar 

  61. Wilson AD, Lester DG, Oberle CS (2005) Application of conductive polymer analysis for wood and woody plant identifications. For Ecol Manag 209:207–224. doi:10.1016/j.foreco.2005.01.030

    Article  Google Scholar 

  62. Witten IH, Hall MA (2011) Data mining practical machine learning tools and techniques. Morgan Kaufmann, Burlington

    Google Scholar 

  63. Zhou T, Wang L, Jionghua T (2008) Pattern recognition of the universal electronic nose. In: Second international symposium on intelligent information technology application, 20–22 Dec 2008, vol 3, pp 249–253

Download references

Acknowledgments

This work was supported by an NSF CAREER GRANT #0846063 and an NSF Graduate Research Fellowship for the first author. We thank Frank Li at Google and Garnet Hertz at UCI for fabrication help, the STAR Group at UCI for providing valuable feedback on drafts. Peter Thomas, Anind Dey, and the anonymous PUC reviewers have greatly strengthened this paper, and for that we are grateful.

Author information

Authors and Affiliations

  1. Department of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, USA

    Sen H. Hirano & Gillian R. Hayes

  2. Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, USA

    Khai N. Truong

Authors
  1. Sen H. Hirano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gillian R. Hayes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khai N. Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sen H. Hirano.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hirano, S.H., Hayes, G.R. & Truong, K.N. uSmell: exploring the potential for gas sensors to classify odors in ubicomp applications relative to airflow and distance. Pers Ubiquit Comput 19, 189–202 (2015). https://doi.org/10.1007/s00779-014-0770-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-014-0770-7

Keywords

Search

Navigation