Skip to main content

Collecting Heart Rate Using a High Precision, Non-contact, Single-Point Infrared Temperature Sensor

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7621)

Abstract

Remotely detecting the physiological state of humans is becoming increasingly important for rehabilitative robotics (RR) and socially assistive robotics (SAR) because it makes robots better-suited to work more closely and more cooperatively with humans. This research delivers a new non-contact technique for detecting heart rate in real time using a high precision, single-point infrared sensor. The proposed approach is an important potential improvement over existing methods because it collects heart rate information unencumbered by biofeedback sensors, complex computational processing or high cost equipment. We use a thermal infrared sensor to capture subtle changes in the sub-nasal skin surface temperature to monitor cardiac pulse. This study extends our previous research in which breathing rate is automatically extracted using the same hardware. Experiments conducted to test the proposed system accuracy show that in 72.7% of typical cases heart rate was successfully detected within 0-9 beats per minute as measured by root-mean-square error.

Keywords

  • Heart Rate
  • Heart Rate Variability
  • Discrete Wavelet Transform
  • Galvanic Skin Response
  • Infrared Sensor

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Krebs, H.I., Hogan, N.: Therapeutic robotics a technology push. Proceedings of the IEEE 94(9), 1727–1738 (2006)

    CrossRef  Google Scholar 

  2. Montagu, J.D., Coles, E.M.: Mechanism and measurement of the galvanic skin response. Psychological Bulletin 65(5), 261–279 (1966)

    CrossRef  Google Scholar 

  3. Tyson, P.D.: Task-related stress and EEG alpha biofeedback. Biofeedback and Self-Regulation 12(2), 105–119 (1987)

    CrossRef  Google Scholar 

  4. Boccanfuso, L., O’Kane, J.M.: Remote measurement of breathing rate in real time using a high precision, single-point infrared temperature sensor. In: Proceedings IEEE International Conference on Biomedical Robotics and Biomechatronics (2012)

    Google Scholar 

  5. Matsukawa, T., Yokoyama, K.: Visualizing physiological information based on 3dcg. FORMA 25, 11–14 (2010)

    Google Scholar 

  6. Barreto, A., Zhai, J., Adjouadi, M.: Non-intrusive Physiological Monitoring for Automated Stress Detection in Human-Computer Interaction. In: Lew, M., Sebe, N., Huang, T.S., Bakker, E.M. (eds.) HCI 2007. LNCS, vol. 4796, pp. 29–38. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  7. Kakadiaris, I.A., Passalis, G., Theoharis, T., Toderici, G., Konstantinidis, I., Murtuza, N.: Multimodal face recognition: Combination of geometry with physiological information. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1022–1029 (2005)

    Google Scholar 

  8. Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transactions on Biomedical Engineering 58(1), 7–11 (2011)

    CrossRef  Google Scholar 

  9. Suzuki, S., Matsui, T., Gotoh, S., Mori, Y., Takase, B., Ishihara, M.: Development of non-contact monitoring system of heart rate variability (hrv) - an approach of remote sensing for ubiquitous technology. In: Proceedings of the International Conference on Ergonomics and Health Aspects of Work with Computers: Held as Part of HCI International, pp. 195–203 (2009)

    Google Scholar 

  10. Kulic, D., Croft, E.: Estimating robot induced affective state using hidden markov models. In: The IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 257–262 (September 2006)

    Google Scholar 

  11. Kulic, D., Croft, E.: Physiological and subjective responses to articulated robot motion. Robotica 25(1), 13–27 (2007)

    CrossRef  Google Scholar 

  12. Rani, P., Sims, J., Brackin, R., Sarkar, N.: Online stress detection using psychophysiological signals for implicit human-robot cooperation. Robotica 20(6), 673–685 (2002)

    CrossRef  Google Scholar 

  13. McLaughlin, L.: Engineering meets emotion: New robots sense human distress. IEEE Intelligent Systems 18, 4–7 (2003)

    Google Scholar 

  14. Liu, C., Rani, P., Sarkar, N.: Affective state recognition and adaptation in human-robot interaction: A design approach. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3099–3106 (2006)

    Google Scholar 

  15. Rohrbaugh, J.W., Sirevaag, E.J., Singla, N., O’Sullivan, J., Chen, M.: Laser doppler vibrometry measures of physiological function: evaluation of biometric capabilities. IEEE Transactions on Information Forensics and Security 5, 449–460 (2010)

    CrossRef  Google Scholar 

  16. A mini-robot with a long standoff capability to detect motion and breathing inside a compound (September 2011), http://www.ereleases.com/pr/author/tialinx-inc (accessed January 2012)

  17. Sekine, M., Maeno, K.: Non-contact heart rate detection using periodic variation in doppler frequency. In: Proceedings of the IEEE, Sensors Applications Symposium (SAS), pp. 318–322 (February 2011)

    Google Scholar 

  18. Lohman, B., Boric-Lubecke, O., Lubecke, V.M., Ong, P.W., Sondhi, M.M.: A digital signal processor for doppler radar sensing of vital signs. IEEE Engineering in Medicine and Biology Magazine 21(5), 161–164 (2002)

    CrossRef  Google Scholar 

  19. Singh, A., Lubecke, V.M.: Respiratory monitoring and clutter rejection using a cw doppler radar with passive rf tags. IEEE Sensors Journal 12(3), 558–565 (2012)

    CrossRef  Google Scholar 

  20. McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3), 289–294 (2003)

    CrossRef  Google Scholar 

  21. Burrus, C.S., Gopinath, R.A., Guo, H.: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall (1997)

    Google Scholar 

  22. Daubechies, I.: Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boccanfuso, L., Perez, E.J., Robinson, M., O’Kane, J.M. (2012). Collecting Heart Rate Using a High Precision, Non-contact, Single-Point Infrared Temperature Sensor. In: Ge, S.S., Khatib, O., Cabibihan, JJ., Simmons, R., Williams, MA. (eds) Social Robotics. ICSR 2012. Lecture Notes in Computer Science(), vol 7621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34103-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34103-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34102-1

  • Online ISBN: 978-3-642-34103-8

  • eBook Packages: Computer ScienceComputer Science (R0)