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Apply Autocorrelation and Forward Difference to Measure Vital Signs Using Ordinary Camera

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Smart Health (ICSH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8549))

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Abstract

Measuring heart rate by portable equipments becomes more and more popular. Current methods such as wavelet, fast fourier transform, peak detection, have been used to analyze heart rate. However, in some cases these methods are ineffective. For example, as a denoising tool, wavelet is not necessary in a few cases. One of the main challenges is determining an effective size of sliding window for heart rate detection when using peak detection. In addition, the time complexity of fast fourier transform is large which can increase the processing time that is not desirable for real-time heart rate detection systems. In this paper, we introduce autocorrelation and forward difference to count heart rate based on the features of cardiac cycle. The results show that our method is good enough so that it can be applied to non-invasive health state detection. And the time complexity of our method is satisfactory.

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References

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Gansu, China

    Letian Sun, Li Liu, Ye Wei, Jun Zhong & Dashi Luo

  2. School of Computing, National University of Singapore, Singapore

    Li Liu

  3. Faculty of Computer and Information Science, Southwest University, Chongqing, China

    Ming Liu

  4. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

    Hamed Monkaresi

Authors
  1. Letian Sun
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  2. Li Liu
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  3. Ye Wei
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  4. Jun Zhong
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  5. Dashi Luo
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  6. Ming Liu
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  7. Hamed Monkaresi
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Editor information

Editors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Xiaolong Zheng

  2. The University of Arizona, Tucson, AZ, USA and Chinese Academy of Sciences,, Beijing, China

    Daniel Zeng

  3. The University of Arizona, Tucson, AZ, USA and Tsinghua University, Beijing,, China

    Hsinchun Chen

  4. Tsinghua University, Beijing, China

    Yong Zhang  & Chunxiao Xing  & 

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Daniel B. Neill

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Sun, L. et al. (2014). Apply Autocorrelation and Forward Difference to Measure Vital Signs Using Ordinary Camera. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds) Smart Health. ICSH 2014. Lecture Notes in Computer Science, vol 8549. Springer, Cham. https://doi.org/10.1007/978-3-319-08416-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-08416-9_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08415-2

  • Online ISBN: 978-3-319-08416-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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