Skip to main content
SpringerLink
Log in

Review on Heart-Rate Estimation from Photoplethysmography and Accelerometer Signals During Physical Exercise

  • Review Article
  • Published:
Journal of the Indian Institute of Science Aims and scope

Abstract

Non-invasive monitoring of physiological signals during physical exercise is essential to customize the exercise module. Photoplethysmography (PPG) signal has often been used to non-invasively monitor heart-rate, respiratory rate, and blood-pressure among other physiological signals. Typically, PPG signal is acquired using pulse oximeter from finger-tip or wrist. Advantage of wrist-based PPG sensors is that it is more convenient to wear. Other sensors such as accelerometer can also be integrated with it due to large area on the wrist. This article provides a review of the algorithms developed for heart rate estimation during physical exercise from the PPG signals and accelerometer signals. The datasets used to develop these techniques are described. Algorithms for denoising of PPG signals using accelerometer signals are either in time domain or frequency domain.

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

Figure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure 6:

Similar content being viewed by others

Notes

  1. http://www.zhilinzhang.com/spcup2015.

References

  1. Selvaraj N, Jaryal A et al (2008) Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. J Med Eng Technol 32(6):479–484

    Article  Google Scholar 

  2. Mendelson Y, Ochs BD (1988) Noninvasive pulse oximetry utilizing skin reflectance photoplethysmography. IEEE Trans Biomed Eng 35(10):798–805

    Article  Google Scholar 

  3. Dubey H, Kumaresan R, Mankodiya K (2016) Harmonic sum-based method for heart rate estimation using PPG signals affected with motion artifacts. J Ambient Intell Hum Comput 1–14

  4. Tamura T, Maeda Y et al (2014) Wearable photoplethysmographic sensorspast and present. Electronics 3(2):282–302

    Article  Google Scholar 

  5. Lee J, Jung W et al (2004) Design of filter to reject motion artifact of pulse oximetry. Comput Stand Interf 26(3):241–249

    Article  Google Scholar 

  6. Han H, Kim M-J, Kim J (2007) Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography. In: 29th annual international conference of the IEEE, Engineering in medicine and biology society, 2007, EMBS 2007. IEEE, New Jersey, pp 1538–1541

  7. Yao J, Warren S (2005) A short study to assess the potential of independent component analysis for motion artifact separation in wearable pulse oximeter signals. In: 27th Annual international conference of the engineering in medicine and biology society, 2005, IEEE-EMBS 2005. IEEE, New Jersey, pp 3585–3588

  8. Kim BS, Yoo SK (2006) Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Trans Biomed Eng 53(3):566–568

    Article  Google Scholar 

  9. Krishnan R, Natarajan B, Warren S (2010) Two-stage approach for detection and reduction of motion artifacts in photoplethysmographic data. IEEE Trans Biomed Eng 57(8):1867–1876

    Article  Google Scholar 

  10. Zhang Z, Pi Z, Liu B (2015) TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans Biomed Eng 62(2):522–531

    Article  Google Scholar 

  11. Punchalard R, Benjangkaprasert C et al. (2001) A robust variable step-size LMS-like algorithm for a second-order adaptive IIR notch filter for frequency detection. In: IEEE third workshop on signal processing advances in wireless communications, 2001, (SPAWC’01). IEEE, New Jersey, pp 232–234

  12. Chan K, Zhang Y (2002) Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter. In: Proceedings of IEEE in sensors, 2002, vol 2. IEEE, New Jersey, pp 1343–1346

  13. Han H, Kim J (2012) Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method. Comput Biol Med 42(4):387–393

    Article  Google Scholar 

  14. Wei P, Guo R et al (2008) A new wristband wearable sensor using adaptive reduction filter to reduce motion artifact. In: International conference on information technology and applications in biomedicine, 2008, ITAB 2008. IEEE, New Jersey, pp 278–281

  15. Fukushima H, Kawanaka H et al (2012) Estimating heart rate using wrist-type photoplethysmography and acceleration sensor while running. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2012. IEEE, New Jersey, pp 2901–2904

  16. Wood LB, Asada HH (2006) Active motion artifact reduction for wearable sensors using Laguerre expansion and signal separation. In: 27th Annual international conference of the engineering in medicine and biology society, 2005, IEEE-EMBS 2005. IEEE, New Jersey, pp 3571–3574

  17. Hayes MJ, Smith PR (2001) A new method for pulse oximetry possessing inherent insensitivity to artifact. IEEE Trans Biomed Eng 48(4):452–461

    Article  Google Scholar 

  18. Yan Y-S, Poon CC, Zhang Y-T (2005) Reduction of motion artifact in pulse oximetry by smoothed pseudo Wigner-Ville distribution. J NeuroEng Rehabil 2:3

    Article  Google Scholar 

  19. Raghuram M, Madhav KV et al (2010) Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals. In: 10th International conference on information sciences signal processing and their applications (ISSPA), 2010. IEEE, New Jersey, pp 460–463

  20. Foo JYA (2006) Comparison of wavelet transformation and adaptive filtering in restoring artefact-induced time-related measurement. Biomed Signal Process Control 1(1):93–98

    Article  Google Scholar 

  21. Sun X, Yang P et al (2012) Robust heart beat detection from photoplethysmography interlaced with motion artifacts based on empirical mode decomposition. In: IEEE-EMBS international conference on biomedical and health informatics (BHI), 2012. IEEE, New Jersey, pp 775–778

  22. Pinheiro E, Postolache O, Girão P (2012) Empirical mode decomposition and principal component analysis implementation in processing non-invasive cardiovascular signals. Measurement 45(2):175–181

    Article  Google Scholar 

  23. Ye Y, He W et al (2017) A robust random forest-based approach for heart rate monitoring using photoplethysmography signal contaminated by intense motion artifacts. Sensors 17(2):385

    Article  Google Scholar 

  24. Lee B, Han J et al (2010) Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry. Physiol Meas 31(12):1585

    Article  Google Scholar 

  25. Tarvainen MP, Georgiadis SD et al (2006) Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm. Physiol Meas 27(3):225

    Article  Google Scholar 

  26. Nakajima K, Tamura T, Miike H (1996) Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique. Med Eng Phys 18(5):365–372

    Article  Google Scholar 

  27. Asada HH, Jiang H-H, Gibbs P (2004) Active noise cancellation using MEMS accelerometers for motion-tolerant wearable bio-sensors. In: 26th Annual international conference of the IEEE engineering in medicine and biology society, 2004, IEMBS’04, vol 1. IEEE, New Jersey, pp 2157–2160

  28. Islam MT, Ahmed ST et al (2017) Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal. Healthc Technol Lett

  29. Peng F, Liu H, Wang W (2015) A comb filter based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals. Physiol Meas 36(10):2159

    Article  Google Scholar 

  30. Wadehn F, Carnal D, Loeliger HA (2015) Estimation of heart rate and heart rate variability from pulse oximeter recordings using localized model fitting. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2015. IEEE, New Jersey, pp 3815–3818

  31. Peng F, Zhang Z et al (2014) Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter. Biomed Eng Online 13(1):1

    Article  Google Scholar 

  32. Ye Y, Cheng Y et al (2016) Combining nonlinear adaptive filtering and signal decomposition for motion artifact removal in wearable photoplethysmography. IEEE Sens J 16(19):7133–7141

    Article  Google Scholar 

  33. Zhang Z-H, Liu J et al (2015) A new framework to extract heart rate information from photoplethysmographic (PPG) signals with strong motion artifacts. In: IEEE region 10 conference TENCON 2015–2015. IEEE, New Jersey, pp 1–4

  34. Grisan E, Cantisani G, et al (2015) A supervised learning approach for the robust detection of heart beat in plethysmographic data. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2015. IEEE, New Jersey, pp 5825–5828

  35. Baca A, Biagetti G et al (2015) CARMA: a robust motion artifact reduction algorithm for heart rate monitoring from PPG signals. In: 23rd European signal processing conference (EUSIPCO), 2015. IEEE, New Jersey, pp 2646–2650

  36. Zhang Y, Liu B, Zhang Z (2015) Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography. Biomed Signal Process Control 21:119–125

    Article  Google Scholar 

  37. Frigo G, Fabris M et al (2015) Efficient tracking of heart rate under physical exercise from photoplethysmographic signals. in: IEEE 1st international forum on research and technologies for society and industry leveraging a better tomorrow (RTSI), 2015. IEEE, New Jersey, pp. 306–311

  38. Sun B, Zhang Z (2015) Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory. IEEE Sens J 15(12):7161–7168

    Article  Google Scholar 

  39. Farhadi M, Mashhadi MB, et al (2016) Realtime heart rate monitoring using photoplethysmographic (PPG) signals during intensive physical exercises. bioRxiv 092627

  40. Sun B, Feng H, Zhang Z (2016) A new approach for heart rate monitoring using photoplethysmography signals contaminated by motion artifacts. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, New Jersey, pp 809–813

  41. Zhang Z (2015) Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction. IEEE Trans Biomed Eng 62(8):1902–1910

    Article  Google Scholar 

  42. Zhang Z, Pi Z, Liu B (2014) TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during intensive physical exercise. IEEE Trans Biomed Eng 9294(c):1–10. URL http://www.ncbi.nlm.nih.gov/pubmed/25252274

  43. Xiong J, Cai L et al (2016) Spectral matrix decomposition-based motion artifacts removal in multi-channel PPG sensor signals. IEEE Access 4:3076–3086

    Article  Google Scholar 

  44. Zhao D, Sun Y et al (2017) SFST: a robust framework for heart rate monitoring from photoplethysmography signals during physical activities. Biomed Signal Process Control 33:316–324

    Article  Google Scholar 

  45. Schäck T, Sledz C et al (2015) A new method for heart rate monitoring during physical exercise using photoplethysmographic signals. In: 23rd European signal processing conference (EUSIPCO), 2015. IEEE, New Jersey, pp 2666–2670

  46. Murthy NKL, Madhusudana PC et al (2015) Multiple spectral peak tracking for heart rate monitoring from photoplethysmography signal during intensive physical exercise. IEEE Signal Process Lett 22(12):2391–2395

    Article  Google Scholar 

  47. Ahamed ST, Islam MT (2016) An efficient method for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. In: 5th International conference on informatics, electronics and vision (ICIEV), 2016. IEEE, New Jersey, pp 863–868

  48. Temko A (2015) Estimation of heart rate from photoplethysmography during physical exercise using Wiener filtering and the phase vocoder. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2015. IEEE, New Jersey, pp 1500–1503

  49. Salehizadeh S, Dao D et al (2015) A novel time-varying spectral filtering algorithm for reconstruction of motion artifact corrupted heart rate signals during intense physical activities using a wearable photoplethysmogram sensor. Sensors 16(1):10

    Article  Google Scholar 

  50. Torres JMM, Ghosh A et al (2016) Heal-T: an efficient PPG-based heart-rate and IBI estimation method during physical exercise. In: 24th European signal processing conference (EUSIPCO), 2016. IEEE, New Jersey, pp 1438–1442

  51. Chowdhury S, Hyder R et al (2016) Real time robust heart rate estimation from wrist-type PPG signals using multiple reference adaptive noise cancellation. IEEE J Biomed Health Inform

  52. Fallet S, Vesin J-M (2015) Adaptive frequency tracking for robust heart rate estimation using wrist-type photoplethysmographic signals during physical exercise. In: 2015 Computing in cardiology conference (CinC), IEEE, New Jersey, pp 925–928

  53. Khan E, Al Hossain F et al (2016) A robust heart rate monitoring scheme using photoplethysmographic signals corrupted by intense motion artifacts. IEEE Trans Biomed Eng 63(3):550–562

    Article  Google Scholar 

  54. Mashhadi MB, Asadi E et al (2016) Heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise with simultaneous accelerometry. IEEE Signal Process Lett 23(2):227–231

    Article  Google Scholar 

  55. Fallet S, Vesin J-M (2017) Robust heart rate estimation using wrist-type photoplethysmographic signals during physical exercise: an approach based on adaptive filtering. Physiol Meas 38(2):155

    Article  Google Scholar 

  56. Alqaraawi A, Alwosheel A, Alasaad A (2016) Towards efficient heart rate variability estimation in artifact-induced Photoplethysmography signals. In: IEEE Canadian conference on electrical and computer engineering (CCECE), 2016. IEEE, New Jersey, pp 1–6

  57. Alqaraawi A, Alwosheel A, Alasaad A (2016) Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach. Healthc Technol Lett 3:136–142

    Article  Google Scholar 

  58. Kazmi SA, Khan S et al (2015) Spectrum analysis of physiological signals of human activities. In: International conference on emerging technologies (ICET), 2015. IEEE, New Jersey, pp 1–6

  59. Wang Y, Liu Z, Dong B (2016) Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision. In: IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), 2016. IEEE, New Jersey, pp 3511–3514

  60. Mullan P, Kanzler CM et al (2015) Unobtrusive heart rate estimation during physical exercise using photoplethysmographic and acceleration data. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2015. IEEE, NEw Jersey, pp 6114–6117

  61. Hayashi T, Ooi T (2016) Estimation of heart rate during exercise from a photoplethysmographic signal considering exercise intensity. Sens Mater 28(4):341–348

    Google Scholar 

  62. Zheng Y, Poon CC et al (2016) Pulse arrival time based cuff-less and 24-h wearable blood pressure monitoring and its diagnostic value in hypertension. J Med Syst 40(9):195

    Article  Google Scholar 

  63. Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28(3):R1

    Article  Google Scholar 

  64. Jarchi D, Casson AJ (2016) Description of a database containing wrist PPG signals recorded during physical exercise with both accelerometer and gyroscope measures of motion. Data 2(1):1

    Article  Google Scholar 

  65. Pu L, Chacon PJ et al (2017) Novel tailoring algorithm for abrupt motion artifact removal in photoplethysmogram signals. Biomed Eng Lett 1–6

  66. Standard A (1993) Z136. 1. American national standard for the safe use of lasers. American National Standards Institute. Inc, New York

    Google Scholar 

Download references

Acknowledgements

This work was financially supported from the Tier 2 research grant funded by Ministry of Education in Singapore (ARC2/15: M4020238).

Author information

Authors and Affiliations

  1. School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, 637459, Singapore

    Vijitha Periyasamy & Manojit Pramanik

  2. Electrical Engineering, Indian Institute of Science, Bangalore, 560012, India

    Prasanta Kumar Ghosh

Authors
  1. Vijitha Periyasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manojit Pramanik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prasanta Kumar Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Manojit Pramanik or Prasanta Kumar Ghosh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Periyasamy, V., Pramanik, M. & Ghosh, P.K. Review on Heart-Rate Estimation from Photoplethysmography and Accelerometer Signals During Physical Exercise. J Indian Inst Sci 97, 313–324 (2017). https://doi.org/10.1007/s41745-017-0037-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41745-017-0037-1

Keywords

Search

Navigation