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I am doing a paper review about 'Non-Contact Video-Based Estimation Of Physiological Parameters: A Review'. It is not finished at all, but I can copy/paste a piece of text about breath information.
Poh et al. [96], explored the possibility to measure Hear Rate (HR) from face videos recorded by a webcam. They detected the region of interest (ROI, i.e. the face area) using Viola-Jones face detector and computed the mean pixel values of the ROI of each frame from three color channels. Then, Independent Component Analysis (ICA) was applied to separate the PPG signal from the three color traces, and the PPG signal was transferred into frequency domain to find the frequency with the max power within the range of [0.7, 4] Hz as the HR frequency. Indeed, ICA had previously been used to reduce motion artifacts in PPG measurements [124], [125]. According to previous findings [120], the green channel trace contains the strongest plethysmographic signal among the three color channels. Poh’s results showed that comparing to the raw green trace, ICA separated sources can achieve higher accuracy for measuring HR.
Later on, Poh et al. [97] extended their original work in order to estimate respiratory rate, which was estimated with a well-known indirect method [126] based on heart rate variability (HRV). The peaks of the PPG waveform (corresponding to the dominant ICA component) were identified to derive a time series of inter-beat intervals. Respiratory rate estimation from HRV works well in healthy young volunteers, but is much less likely to give accurate results in elderly subjects, especially those with chronic diseases, most of which depress autonomic function [127].
However, breathing is associated with movement of the upper thorax and regions of the face. The changes in the amplitude of the PPG waveform caused by breathing-synchronous motion can be extracted though band-pass filtering and spectral analysis [112].
Respiratory rate can also be estimated directly through motion-tracking techniques. Eulerian video magnification techniques can be used to track and amplify the motion-related changes caused by breathing in the videos of human subjects, including neonates, recorded under normal lighting conditions [100].
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Westphal et al. [128] examine abilities and limitations of the aforementioned algorithm proposed by Wu et al. [100]. More specifically, this study investigates the influence of varying ambient light as well as the influence of movements of the measuring objects on the results. Reliable results are strongly dependent on the right environmental conditions. Good results are obtained if the ROI is nearly free of movements. However, particular movements of the subject have a greater influence on the accuracy. The effect of varying ambient light was found to have no significant influence on the accuracy of the result.
[96] M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation,” Optics express, vol. 18, no. 10, pp. 10 762–10 774, 2010.
[124] J. Yao and S. Warren, “A short study to assess the potential of independent component analysis for motion artifact separation in wearable pulse oximeter signals,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. IEEE, 2005, pp. 3585–3588.
[125] B. S. Kim and S. K. Yoo, “Motion artifact reduction in photoplethysmography using independent component analysis,” Biomedical Engineering, IEEE Transactions on, vol. 53, no. 3, pp. 566–568, 2006.
[120] W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethysmographic imaging using ambient light,” Optics express, vol. 16, no. 26, pp. 21 434–21 445, 2008.
[97] M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” Biomedical Engineering, IEEE Transactions on, vol. 58, no. 1, pp. 7–11, 2011.
[126] B. M. SAYKRS, “Analysis of heart rate variability,” Ergonomics, vol. 16, no. 1, pp. 17–32, 1973.
[127] D. F. Dietrich, C. Schindler, J. Schwartz, J.-C. Barth´el´emy, J.-M. Tschopp, F. Roche, A. von Eckardstein, O. Br¨andli, P. Leuenberger, D. R. Gold et al., “Heart rate variability in an ageing population and its association with lifestyle and cardiovascular risk factors: results of the sapaldia study,” Europace, vol. 8, no. 7, pp. 521–529, 2006.
[112] L. Tarassenko, M. Villarroel, A. Guazzi, J. Jorge, D. Clifton, and C. Pugh, “Non-contact video-based vital sign monitoring using ambient light and auto-regressive models,” Physiological measurement, vol. 35, no. 5, p. 807, 2014.
[100] H.-Y. Wu, M. Rubinstein, E. Shih, J. V. Guttag, F. Durand, and W. T. Freeman, “Eulerian video magnification for revealing subtle change
[128] M. Westphal, “Examination of a novel method for non-contact, low-cost, and automated heart-rate detection in ambient light using photoplethysmographic imaging,” DTIC Document, Tech. Rep., 2014.
