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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© 2014 Springer International Publishing Switzerland
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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
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