Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification

Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simone Orcioni, Claudio Turchetti

Abstract

Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects’ wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.

References

  1. A., Biagetti, G., Camilletti, M., Crippa, P., Falaschetti, L., Orcioni, S., Rossini, L., Tonelli, D., and Turchetti, C. (2015). CARMA: A robust motion artifact reduction algorithm for heart rate monitoring from PPG signals. In 23rd European Signal Processing Conference (EUSIPCO 2015), pages 2696-2700.
  2. Biagetti, G., Crippa, P., Curzi, A., Orcioni, S., and Turchetti, C. (2015). Speaker identification with short sequences of speech frames. In 4th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2015), volume 2, pages 178-185.
  3. Figueiredo, M. A. F. and Jain, A. K. (2002). Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3):381-396.
  4. Foo, J. Y. A. (2006). Comparison of wavelet transformation and adaptive filtering in restoring artefact-induced time-related measurement. Biomedical Signal Processing and Control, 1(1):93-98.
  5. Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Academic Press.
  6. Gibbs, P. T., Wood, L. B., and Asada, H. H. (2005). Active motion artifact cancellation for wearable health monitoring sensors using collocated MEMS accelerometers. In Smart Structures and Materials, volume 5765, pages 811-819. International Society for Optics and Photonics.
  7. Jain, A. K., Duin, R. P. W., and Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4- 37.
  8. Kim, B. S. and Yoo, S. K. (2006). Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Transactions on Biomedical Engineering, 53(3):566-568.
  9. Lee, B., Han, J., Baek, H. J., Shin, J. H., Park, K. S., and Yi, W. J. (2010). Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry. Physiological Measurement, 31(12):1585.
  10. Raghuram, M., Madhav, K. V., Krishna, E. H., Komalla, N. R., Sivani, K., and Reddy, K. A. (2012). HHT based signal decomposition for reduction of motion artifacts in photoplethysmographic signals. In IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC), pages 1730-1734.
  11. Raghuram, M., Madhav, K. V., Krishna, E. H., and Reddy, K. A. (2010). Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals. In 10th Int. Conf. Information Sciences Signal Processing and their Applications (ISSPA), pages 460-463.
  12. Raghuram, M., Sivani, K., and Reddy, K. A. (2014). E2MD for reduction of motion artifacts from photoplethysmographic signals. In Int. Conf. Electronics and Communication Systems (ICECS), pages 1-6.
  13. Ram, M. R., Madhav, K. V., Krishna, E. H., Komalla, N. R., and Reddy, K. A. (2012). A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter. IEEE Transactions on Instrumentation and Measurement, 61(5):1445-1457.
  14. Reynolds, D. and Rose, R. (1995). Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on speech and audio processing, 3(1):72-83.
  15. Zhang, Z., Pi, Z., and Liu, B. (2015). TROIKA: A general framework for heart rate monitoring using wristtype photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 62(2):522-531.
Download


Paper Citation


in Harvard Style

Biagetti G., Crippa P., Falaschetti L., Orcioni S. and Turchetti C. (2016). Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 467-474. DOI: 10.5220/0005755304670474


in Bibtex Style

@conference{icpram16,
author={Giorgio Biagetti and Paolo Crippa and Laura Falaschetti and Simone Orcioni and Claudio Turchetti},
title={Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={467-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005755304670474},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification
SN - 978-989-758-173-1
AU - Biagetti G.
AU - Crippa P.
AU - Falaschetti L.
AU - Orcioni S.
AU - Turchetti C.
PY - 2016
SP - 467
EP - 474
DO - 10.5220/0005755304670474