Skip to main content

Non-invasive Multi-modal Human Identification System Combining ECG, GSR, and Airflow Biosignals


A huge amount of data can be collected through a wide variety of sensor technologies. Data mining techniques are often useful for the analysis of gathered data. This paper studies the use of three wearable sensors that monitor the electrocardiogram, airflow, and galvanic skin response of a subject with the purpose of designing an efficient multi-modal human identification system. The proposed system, based on the rotation forest ensemble algorithm, offers a high accuracy (99.6 % true acceptance rate and just 0.1 % false positive rate). For its evaluation, the proposed system was testing against the characteristics commonly demanded in a biometric system, including universality, uniqueness, permanence, and acceptance. Finally, a proof-of-concept implementation of the system is demonstrated on a smartphone and its performance is evaluated in terms of processing speed and power consumption. The identification of a sample is extremely efficient, taking around 200 ms and consuming just a few millijoules. It is thus feasible to use the proposed system on a regular smartphone for user identification.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors, 13, 17472–17500.

    Article  Google Scholar 

  2. Rostami, M., Juels, A., & Koushanfar, F. (2013). Heart-to-heart (H2H): authentication for implanted medical devices. In Proceedings of the ACM SIGSAC conference on computer & communications security (pp. 1099–1112).

  3. Rasmussen, K. B., Roeschlin, M., Martinovic, I., & Tsudik, G. (2014). Authentication using pulse-response biometrics. In Proceedings of the network and distributed system security symposium (NDSS).

  4. Eng, A., & Wahsheh, L. (2013). Look into my eyes: A survey of biometric security. In Proceedings of the tenth international conference on information technology: New generations (pp. 422–427).

  5. Odinaka, I., Po-Hsiang, L., Kaplan, A. D., O’Sullivan, J. A., Sirevaag, E. J., & Rohrbaugh, J. W. (2012). ECG biometric recognition: A comparative analysis. IEEE Transactions on Information Forensics and Security, 7, 1812–1824.

    Article  Google Scholar 

  6. Khalifa, W., Salem, A., Roushdy, M., & Revett, K. (2012). A survey of EEG based user authentication schemes. In Proceedings of the 8th international conference on informatics and systems (pp. BIO-55–BIO-60).

  7. Spachos, P., Gao, J., & Hatzinakos, D. (2011).Feasibility study of photoplethysmographic signals for biometric identification. In Proceedings of the 17th international conference on digital signal processing (DSP) (pp. 1–5).

  8. Ichino, M., Sakano, H., & Komatsu, N. (2006). Multimodal biometrics of lip movements and voice using kernel fisher discriminant analysis. In Proceedings of the international conference on control, automation, robotics and vision (pp. 1–6).

  9. Jani, R., & Agrawal, N. (2013). Proposed framework for enhancing security in fingerprint and finger-vein multimodal biometric recognition. In Proceedings of the international conference on machine intelligence and research advancement (pp. 440–444).

  10. Revett, K., Deravi, F., & Sirlantzis, K. (2010). Biosignals for user authentication—Towards cognitive biometrics. In Proceedings of the international conference on emerging security technologies (EST) (pp. 71–76).

  11. Riera, A., Dunne, S., Cester, I., & Ruffini, G. (2008). STARFAST: A wireless wearable EEG/ECG biometric system based on the ENOBIO Sensor. In Proceedings of the international workshop on wearable micro and nanosystems for personalised health (pp. 1–4).

  12. Simon, B. P., & Eswaran, C. (1997). An ECG classifier de- signed using modified decision based neural networks. Computer and Biomedical Research, 30, 257–272.

    Article  Google Scholar 

  13. Doorly, D. J., Taylor, D. J., & Schroter, R. C. (2008). Mechanics of airflow in the human nasal airways. Respiratory Physiology & Neurobiology, 163, 100–110.

    Article  Google Scholar 

  14. Chaaban, M., & Corey, J. P. (2011). Assessing Nasal Air Flow. Proceedings of the American Thoracic Society, 8, 70–78.

    Article  Google Scholar 

  15. Ogorevc, J., Gersak, G., Novak, D., & Drnovsek, J. (2013). Metrological evaluation of skin conductance measurements. Measurement, 46, 2993–3001.

    Article  Google Scholar 

  16. Clifford, G. D., Azuaje, F., & McSharry, P. (2006). Advanced methods and tools for ECG data analysis. Norwood, MA: Artech House Inc.

    Google Scholar 

  17. Abellán, J., & Mantas, C. J. (2014). Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 41, 3825–3830.

    Article  Google Scholar 

  18. Rahman, A., & Verma, B. (2013). Effect of ensemble classifier composition on offline cursive character recognition. Information Processing and Management, 49, 852–864.

    Article  Google Scholar 

  19. Ozçift, A. (2011). Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Computers in Biology and Medicine, 41, 265–271.

    Article  Google Scholar 

  20. Yin, X. C., Huang, K., Hao, H. W., Iqbal, K., & Wang, Z. B. (2014). A novel classifier ensemble method with sparsity and diversity. Neurocomputing, 134, 214–221.

    Article  Google Scholar 

  21. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.

    MATH  MathSciNet  Google Scholar 

  22. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.

    MATH  MathSciNet  Article  Google Scholar 

  23. Cao, Y., Miao, Q. G., Liu, J. C., & Gao, L. (2013). Advance and prospects of adaboost algorithm. Acta Automatica Sinica, 39, 745–758.

    Article  Google Scholar 

  24. Brown, G., Wyatt, J. L., Harris, R., & Yao, X. (2005). Diversity creation methods: A survey and categorization. Information Fusion, 6, 5–20.

    Article  Google Scholar 

  25. Rokach, L. (2010). Pattern Classification Using Ensemble Methods. River Edge: World Scientific.

    MATH  Google Scholar 

  26. Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1619–1630.

    Article  Google Scholar 

  27. Pal, S., & Mitra, M. (2011). ECG based biometric authentication: a novel data modeling approach. In Proceedings of the international conference on image information processing (ICIIP) (pp. 1–4).

  28. Singh, Y., & Singh, S. (2012). Evaluation of electrocardiogram for biometric authentication. Journal of Information Security, 3, 39–48.

    Article  Google Scholar 

  29. Shen, T. W., Tompkins, W. J., & Hu, Y. H. (2002). One-lead ECG for identity verification. In Proceedings of the 24th annual conference and the annual fall meeting of the biomedical engineering society EMBS/BMES conference (Vol. 1, pp. 62–63).

  30. Anderson, C. W., & Bratman, J. A. (2008). Translating thoughts into actions by finding patterns in brainwaves. In Proceedings of the fourteenth Yale workshop on adaptive and learning systems (pp. 1–6).

  31. Carmona, N., Rua-Seoane, J., Elorza, J., Saenz de Pipaon, E., Palacios, C., & Bragard, J. (2013). Aging of ECG characteristics over a five year period. In Proceedings of the conference on computing in cardiology (CinC) (pp. 1031–1034).

  32. Schneier, B. (2010). Changing Passwords.

  33. Singh, Y. N., Singh, S. K., & Ray, A. K. (2012). Bioelectrical Signals as emerging biometrics: Issues and challenges. ISRN Signal Processing, 2012, 1–13.

    Article  Google Scholar 

  34. Tantawi, M. M., Revett, K., Tolba, M. F., & Salem, A. (2012). On the use of the electrocardiogram for biometric authentication. In Proceedings of the 8th international conference on informatics and systems (pp. BIO-48–BIO-54).

  35. Suarez-Tangil, G., Tapiador, J. E., Peris-Lopez, P., & Pastrana, S. (2015). Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation. Pervasive and Mobile Computing, 18, 137–151.

    Article  Google Scholar 

  36. Yoon, C., Kim, D., Jung, W., Kang, C., & Cha, H. (2012). Appscope: Application energy metering framework for android smartphone using kernel activity monitoring. In Proceedings of the USENIX annual technical conference (p. 36).

  37. Jung, W., Kang, C., Yoon, C., Kim, D., & Cha, H. (2012). DevScope: A nonintrusive and online power analysis tool for smartphone hardware components. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis (pp. 353–362).

Download references


This work was supported by MINECO grant TIN2013- 46469-R (SPINY: Security and Privacy in the Internet of You) and CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Carmen Camara.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Camara, C., Peris-Lopez, P., Tapiador, J.E. et al. Non-invasive Multi-modal Human Identification System Combining ECG, GSR, and Airflow Biosignals. J. Med. Biol. Eng. 35, 735–748 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Sensor data
  • Bioinformatics
  • Human identification
  • Data mining
  • Ensemble classification