Comparative Study and Analysis of Pulse Rate Measurement by Vowel Speech and EVM

  • Ria PaulEmail author
  • Rahul Shandilya
  • R. K. Sharma
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


The paper presents two noncontact pulse rate measurement techniques from vowel speech signals and Eulerian Video Magnification. The proposed methods use signals those are neither audible nor visible to naked eyes. The signals are recorded and their characteristic plots and spectrum analysis by Short-Time Fourier Transform reveal some peaks from which pulse rate can be calculated. The methods are then compared with the conventional methods where the accuracy differs by only 3.9% for vowel speech and by 0.4% for Eulerian Video Magnification. The Bland–Altman plot for the techniques shows that both are acceptable as they lie between ±1.96 Standard Deviation. The data collected from the methods are processed in MATLAB and also implemented on FPGA using serial communication by RS232.


Biomedical Image processing Eulerian video magnification Vowel speech Short-time Fourier transform FPGA Bland–Altman plot RS232 


  1. 1.
    James AP (2015) Heart rate monitoring using human speech spectral features. Hum-Centric Comput Inf Sci 5(1):33CrossRefGoogle Scholar
  2. 2.
    Mesleh A et al (2012) Heart rate extraction from vowel speech signals. J Comput Sci Technol 27(6):1243–1251CrossRefGoogle Scholar
  3. 3.
    Sukaphat S et al (2016) Heart rate measurement on Android platform. In: 2016 13th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON). IEEE, New York, pp 1–5Google Scholar
  4. 4.
    He X, Goubran RA, Liu XP (2016) Wrist pulse measurement and analysis using Eulerian video magnification. In: 2016 IEEE EMBS international conference on biomedical and health informatics (BHI). IEEE, New York, pp 1–4Google Scholar
  5. 5.
    Aubel C, Stotz D, Bölcskei H (2018) A theory of super-resolution from short-time Fourier transform measurements. J Fourier Anal Appl 24(1):45–107MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang G (2018) Time-phase amplitude spectra based on a modified short-time Fourier transform. Geophys Prospect 66(1):34–46CrossRefGoogle Scholar
  7. 7.
    He X, Goubran RA, Liu XP (2014) Using Eulerian video magnification framework to measure pulse transit time. In: 2014 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, New York, pp 1–4Google Scholar
  8. 8.
    Pokharkar S (2015) FPGA based design and implementation of ECG feature extraction. Int J Adv Found Res Sci Eng (IJAFRSE) 1(12)Google Scholar
  9. 9.
    Altman DG, Bland JM (2017) Assessing agreement between methods of measurement. Clin Chem 63(10):1653–1654CrossRefGoogle Scholar
  10. 10.
    Matsuura N et al (2017) Bland–Altman analysis for method comparisons. Adv Mod Med 354Google Scholar
  11. 11.
    Ryskaliyev A, Askaruly S, James AP (2016) Speech signal analysis for the estimation of heart rates under different emotional states. In: 2016 International conference on advances in computing, communications and informatics (ICACCI). IEEE, New YorkGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of VLSI Design and Embedded SystemsNational Institute of Technology KurukshetraKurukshetraIndia

Personalised recommendations