Pattern Analysis and Applications

, Volume 19, Issue 1, pp 207–220 | Cite as

Odor recognition in robotics applications by discriminative time-series modeling

  • Frank-Michael Schleif
  • Barbara Hammer
  • Javier Gonzalez Monroy
  • Javier Gonzalez Jimenez
  • Jose-Luis Blanco-Claraco
  • Michael Biehl
  • Nicolai Petkov
Industrial and Commercial Application


Odor classification by a robot equipped with an electronic nose (e-nose) is a challenging task for pattern recognition since volatiles have to be classified quickly and reliably even in the case of short measurement sequences, gathered under operation in the field. Signals obtained in these circumstances are characterized by a high-dimensionality, which limits the use of classical classification techniques based on unsupervised and semi-supervised settings, and where predictive variables can be only identified using wrapper or post-processing techniques. In this paper, we consider generative topographic mapping through time (GTM-TT) as an unsupervised model for time-series inspection, based on hidden Markov models regularized by topographic constraints. We further extend the model such that supervised classification and relevance learning can be integrated, resulting in supervised GTM-TT. Then, we evaluate the suitability of this new technique for the odor classification problem in robotics applications. The performance is compared with classical techniques as nearest neighbor, as an absolute baseline, support vector machine and a recent time-series kernel approach, demonstrating the eligibility of our approach for high-dimensional data. Additionally, we exploit the learning system introduced in this work, providing a measure of the relevance of each sensor and individual time points in the classification process, from which important information can be extracted.


Electronic nose Volatile classification Odor recognition  time-series Prototype learning Relevance learning 



The first author likes to thank Peter Tino, University of Birmingham, for interesting discussions about probabilistic modeling and Tien-ho Lin, Carnegie Mellon University, USA for support with the simulation data. Further, Ivan Olier, University of Manchaster, UK; Iain Strachan, AEA Technology, Harwell, UK and Markus Svensen, Microsoft Research, Cambridge, UK for providing code for GTM and GTM-TT. Further we would like to thank Fengzhen Tang, University of Birmingham for providing invaluable  support with the RTK method. This work was supported by the DFG project HA2719/4-1 to BH, by the DFG-NSF project TO 409/8-1, and by the Cluster of Excellence 277 CITEC funded in the framework of the German Excellence Initiative. Further, a Marie Curie Intra-European Fellowship (IEF): FP7-PEOPLE-2012-IEF (FP7-327791-ProMoS)  is gratefully acknowledged.  Additional support was provided by funds from the Andalucía Regional Government and the European Union (FEDER) under research project: TEP08-4016.


  1. 1.
    Astudillo CA, Oommen BJ (2014) Topology-oriented self-organizing maps: a survey. Pattern Anal Appl 17(2):1–26MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bishop CM, Svensén M, Williams CKI (1998) Gtm: the generative topographic mapping. Neural Comput 10(1):215–234CrossRefzbMATHGoogle Scholar
  3. 3.
    Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer, New YorkzbMATHGoogle Scholar
  4. 4.
    Blanco J-L, Monroy JG González-Jiménez, J, Lilienthal A (2013) A Kalman filter based approach to probabilistic gas distribution mapping. In: 28th symposium on applied computing (SAC), March 2013.Google Scholar
  5. 5.
    Brahim-Belhouari S, Bermak A (2005) Gas identification using density models. Pattern Recognit Lett 26(6):699–706CrossRefGoogle Scholar
  6. 6.
    Chen H, Tang F, Tino P, Yao X (2013) Model-based kernel for efficient time-series analysis. In: Proceedings of the 19th ACM SIGKDD conference on knowledge discovery and data mining (KDD) (KDD’13), Chicago, USAGoogle Scholar
  7. 7.
    Costa IG, Schönhuth A, Hafemeister C, Schliep A (2009) Constrained mixture estimation for analysis and robust classification of clinical time-series. Bioinformatics 25(12):i6–i14Google Scholar
  8. 8.
    Distante C, Siciliano P, Persaud KC (2002) Dynamic cluster recognition with multiple self-organising maps. Pattern Anal Appl 5(3):306–315MathSciNetCrossRefGoogle Scholar
  9. 9.
    Distante C, Leo M, Siciliano P, Persaud KC (2002) On the study of feature extraction methods for an electronic nose. Sens Actuators B Chem 87(2):274–288CrossRefGoogle Scholar
  10. 10.
    Douzal-Chouakria A, Amblard C (2012) Classification trees for time-series. Pattern Recognit 45(3):1076–1091CrossRefGoogle Scholar
  11. 11.
    Dumitrescu D, Lazzerini B, Marcelloni F (2000) A fuzzy hierarchical classification system for olfactory signals. Pattern Anal Appl 3(4):325–334MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Ehret B, Safenreiter K, Lorenz F, Biermann J (2011) A new feature extraction method for odour classification. Sens Actuators B Chem 158(1):75–88CrossRefGoogle Scholar
  13. 13.
    Ferri G, Caselli E, Mattoli V, Mondini A, Mazzolai B, Dario P (2009) Spiral: anovel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow. Robot Auton Sys 57(4):393–402CrossRefGoogle Scholar
  14. 14.
    Fonollosa J, Rodrguez-Lujn I, Trincavelli M, Vergara A, Huerta R (2014) Chemical discrimination in turbulent gas mixtures with mox sensors validated by gas chromatography-mass spectrometry. Sensors 14(10):19336–19353CrossRefGoogle Scholar
  15. 15.
    Gisbrecht A, Hammer B (2011) Relevance learning in generative topographic mapping. Neurocomputing 74(9):1359–1371CrossRefGoogle Scholar
  16. 16.
    González-Jiménez J,Monroy JG, Blanco J-L (2013) Robots that can smell: motivation and problems. In: Submitted to 15th international symposium on olfaction and electronic nose (ISOEN)Google Scholar
  17. 17.
    Gonzalez-Jimenez J, Monroy JG, Blanco J-L (2011) The multi-chamber electronic nose. An improved olfaction sensor for mobile robotics. Sensors 11(6):6145–6164Google Scholar
  18. 18.
    Gutierrez-Osuna R (2002) Pattern analysis for machine olfaction: a review. Sens J IEEE 2(3):189–202Google Scholar
  19. 19.
    Hammer B, Villmann TH (2002) Generalized relevance learning vector quantization. Neural Netw 15(8–9):1059–1068CrossRefGoogle Scholar
  20. 20.
    Hatami N, Chira C (2013) Classifiers with a reject option for early time-series classification. In: Proceedings of the IEEE symposium on computational intelligence and ensemble learning, CIEL 2013, IEEE symposium series on computational intelligence (SSCI), 16–19 April 2013. IEEE, Singapore, pp 9–16Google Scholar
  21. 21.
    Lee J, Verleysen M (2005) Generalizations of the lp norm for time-series and its application to self-organizing maps. In: Cottrell M (ed) 5th workshop on self-organizing maps, vol 1, pp 733–740Google Scholar
  22. 22.
    Lilienthal AJ, Reggente M, Trincavelli M, Blanco J-L, Gonzalez J (2009) A statistical approach to gas distribution modelling with mobile robots—the kernel dmv algorithm. In: Intelligent robots and systems, 2009. IROS 2009. IEEE/RSJ international conference, pp 570–576Google Scholar
  23. 23.
    Lin T-H, Kaminski N, Bar-Joseph Z (2008) Alignment and classification of time-series gene expression in clinical studies. In: ISMB, pp 147–155Google Scholar
  24. 24.
    Lisboa PJG (2013) Interpretability in machine learning—principles and practice. In: Masulli F, Pasi G, Yager RR (eds) Fuzzy logic and applications—10th international workshop, WILF 2013, Genoa, Italy, 19–22 Nov 2013. Proceedings, Lecture Notes in Computer Science, vol 8256. Springer, pp 15–21Google Scholar
  25. 25.
    Loutfi A, Coradeschi S, Lilienthal AJ, Gonzalez J (2009) Gas distribution mapping of multiple odour sources using a mobile robot. Robotica 27(2):311–319Google Scholar
  26. 26.
    Marques L, Nunes, U, de Almeida AT (2002) Olfaction-based mobile robot navigation. Thin Solid Films, 418(1):51–58. Proceedings from the international school on gas sensors in conjunction with the 3rd European school of the NOSE networkGoogle Scholar
  27. 27.
    Martinelli E, Magna G, Vergara A, Di Natale C (2014) Cooperative classifiers for reconfigurable sensor arrays. Sens Actuators B Chem 199:83–92CrossRefGoogle Scholar
  28. 28.
    Michalak M (2011) Adaptive kernel approach to the time-series prediction. Pattern Anal Appl 14(3):283–293MathSciNetCrossRefGoogle Scholar
  29. 29.
    Monroy JG, Gonzlez-Jimnez J, Blanco J-L (2012) Overcoming the slow recovery of mox gas sensors through a system modeling approach. Sensors 12(10):13664–13680Google Scholar
  30. 30.
    Moseley P, Tofield B (eds) (1987) Solid state gas sensors. Adam Hilger, BristolGoogle Scholar
  31. 31.
    Olier I, Vellido A (2008) Advances in clustering and visualization of time-series using gtm through time. Neural Netw 21(7):904–913CrossRefzbMATHGoogle Scholar
  32. 32.
    Petitjean F, Ketterlin A, Ganarski P (2011) A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognit 44(3):678–693CrossRefzbMATHGoogle Scholar
  33. 33.
    Prieto OJ, Alonso-González CJ, Rodríguez JJ (2013) Stacking for multivariate time-series classification. Pattern Anal Appl 1–16. doi: 10.1007/s10044-013-0351-9
  34. 34.
    Robotics K. The jaco research edition robotic arm.
  35. 35.
    Sauve AC, Speed TP (2004) Normalization, baseline correction and alignment of high-throughput mass spectrometry data. In: Proceedings of Gensips.
  36. 36.
    Schleif F-M, Gisbrecht A, Hammer B (2012) Relevance learning for short high-dimensional time-series in the life sciences. Proceedings of IJCNN, pp 2069–2076Google Scholar
  37. 37.
    Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012) Learning relevant time points for time-series data in the life sciences. In: Proceedings of ICANN, pp 531–539Google Scholar
  38. 38.
    Schleif F-M, Ongyerth F-M, Villmann T (2009) Supervised data analysis and reliability estimation for spectral data. Neurocomputing 72(16–18):3590–3601CrossRefGoogle Scholar
  39. 39.
    Schneider P, Biehl M, Hammer B (2009) Distance learning in discriminative vector quantization. Neural Comput 21:2942–2969MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Shaffer RE, Rose-Pehrsson SL, Andrew McGill R (1999) A comparison study of chemical sensor array pattern recognition algorithms. Anal Chim Acta 384(3):305–317CrossRefGoogle Scholar
  41. 41.
    Shumway RH, Stoffer DS (2000) Time-series analysis and Its applications. Springer Texts In Statistics. Springer, New YorkCrossRefzbMATHGoogle Scholar
  42. 42.
    Strachan IGD (2002) Latent variable methods for visualization through time. Ph.D. thesis, University of Edinburgh, EdinburghGoogle Scholar
  43. 43.
    Strickert M, Hammer B (2005) Merge SOM for temporal data. Neurocomputing 64:39–72CrossRefGoogle Scholar
  44. 44.
    Szczurek A, Krawczyk B, Maciejewska M (2013) VOCs classification based on the committee of classifiers coupled with single sensor signals. Chemometr Intell Lab Syst 125:1–10CrossRefGoogle Scholar
  45. 45.
    Trincavelli M, Coradeschi S, Loutfi A (2009) Odour classification system for continuous monitoring applications. Sens Actuators B Chem 58:265–273CrossRefGoogle Scholar
  46. 46.
    Vergara A, Fonollosa J, Mahiques J, Trincavelli M, Rulkov N, Huerta R (2013) On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines. Sens Actuators B Chem 185:462–477CrossRefGoogle Scholar
  47. 47.
    Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R (2012) Chemical gas sensor drift compensation using classifier ensembles. Sens Actuators B Chem 166–167:320–329CrossRefGoogle Scholar
  48. 48.
    Welch LR (2003) Hidden Markov models and the Baum–Welch algorithm. IEEE Inf Theory Soc Newsl 53(4).\_dec\_03final.pdf
  49. 49.
    Ziyatdinov A, Fonollosa J, Fernndez L, Gutierrez-Glvez A, Marco S, Perera A (2015) Bioinspired early detection through gas flow modulation in chemo-sensory systems. Sens Actuators B Chem 206:538–547CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Frank-Michael Schleif
    • 1
  • Barbara Hammer
    • 2
  • Javier Gonzalez Monroy
    • 3
  • Javier Gonzalez Jimenez
    • 3
  • Jose-Luis Blanco-Claraco
    • 4
  • Michael Biehl
    • 5
  • Nicolai Petkov
    • 5
  1. 1.School of Computer ScienceUniversity of BirminghamBirminghamUK
  2. 2.Center of ExcellenceUniversity of BielefeldBielefeldGermany
  3. 3.Dpto. Ingenieria de Sistemas y Automatica E.T.S.I. Informatica, TelecomunicacionUniversidad de MalagaMalagaSpain
  4. 4.Universidad de AlmeriaLa CaadaSpain
  5. 5.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenGroningenThe Netherlands

Personalised recommendations