• Acta Optica Sinica
  • Vol. 43, Issue 15, 1512002 (2023)
Lingqin Kong1、2、3, Yuejin Zhao1、2、3、*, Liquan Dong1、2、3, Ming Liu1、2、3, Ge Xu1, Mei Hui1, and Xuhong Chu1、2、3
Author Affiliations
  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
  • 2Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, Beijing 100081, China
  • 3Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314019, Zhejiang, China
  • show less
    DOI: 10.3788/AOS230755 Cite this Article Set citation alerts
    Lingqin Kong, Yuejin Zhao, Liquan Dong, Ming Liu, Ge Xu, Mei Hui, Xuhong Chu. Non-Contact Physiological Parameter Detection and Its Application Based on Imaging Photoplethysmography[J]. Acta Optica Sinica, 2023, 43(15): 1512002 Copy Citation Text show less
    References

    [1] Loukogeorgakis S, Dawson R, Phillips N et al. Validation of a device to measure arterial pulse wave velocity by a photoplethysmographic method[J]. Physiological Measurement, 23, 581-596(2002).

    [2] Hertzman A B, Spealman C R. Observation on the finger volume pulse recorded photoelectrically[J]. The American Journal of Physiology, 119, 334-335(1937).

    [3] Hertzman A B. The blood supply of various skin areas as estimated by the photoelectric plethysmograph[J]. American Journal of Physiology-Legacy Content, 124, 328-340(1938).

    [4] Sanyal S, Nundy K K. Algorithms for monitoring heart rate and respiratory rate from the video of a user’s face[J]. IEEE Journal of Translational Engineering in Health and Medicine, 6, 2700111(2018).

    [5] Poh M Z, McDuff D J, Picard R W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation[J]. Optics Express, 18, 10762-10774(2010).

    [6] Prathosh A P, Praveena P, Mestha L K et al. Estimation of respiratory pattern from video using selective ensemble aggregation[J]. IEEE Transactions on Signal Processing, 65, 2902-2916(2017).

    [7] Rasche S, Trumpp A, Waldow T et al. Camera-based photoplethysmography in critical care patients[J]. Clinical Hemorheology and Microcirculation, 64, 77-90(2016).

    [8] Soleimani V, Mirmehdi M, Damen D M et al. Depth-based whole body photoplethysmography in remote pulmonary function testing[J]. IEEE Transactions on Biomedical Engineering, 65, 1421-1431(2017).

    [9] Allen J. Photoplethysmography and its application in clinical physiological measurement[J]. Physiological Measurement, 28, R1-R39(2007).

    [10] Bortolotto L A, Blacher J, Kondo T et al. Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity[J]. American Journal of Hypertension, 13, 165-171(2000).

    [11] Miyai N, Miyashita K, Arita M et al. Noninvasive assessment of arterial distensibility in adolescents using the second derivative of photoplethysmogram waveform[J]. European Journal of Applied Physiology, 86, 119-124(2001).

    [12] Verkruysse W, Svaasand L O, Nelson J S. Remote plethysmographic imaging using ambient light[J]. Optics Express, 16, 21434-21445(2008).

    [13] Kamshilin A A, Margaryants N B. Origin of photoplethysmographic waveform at green light[J]. Physics Procedia, 86, 72-80(2017).

    [14] Kamshilin A A, Nippolainen E, Sidorov I S et al. A new look at the essence of the imaging photoplethysmography[J]. Scientific Reports, 5, 10494(2015).

    [15] Kwon S, Kim J, Lee D et al. ROI analysis for remote photoplethysmography on facial video[C], 4938-4941(2015).

    [16] Alian A A, Shelley K H. Photoplethysmography[J]. Best Practice & Research Clinical Anaesthesiology, 28, 395-406(2014).

    [17] Sun Y, Azorin-Peris V, Kalawsky R et al. Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam[J]. Journal of Biomedical Optics, 17, 037005(2012).

    [18] Hanzlik P J, Deeds F, Terada B. A simple method of demonstrating changes in blood supply of the ear and effects of some measures[J]. Journal of Pharmacology and Experimental Therapeutics, 56, 194-204(1936).

    [19] Reisner A, Shaltis P A, McCombie D et al. Utility of the photoplethysmogram in circulatory monitoring[J]. Anesthesiology, 108, 950-958(2008).

    [20] Wang W. Robust and automatic remote photoplethysmography[D](2017).

    [21] Volkov M V, Margaryants N B, Potemkin A V et al. Video capillaroscopy clarifies mechanism of the photoplethysmographic waveform appearance[J]. Scientific Reports, 7, 13298(2017).

    [22] Nitzan M, Adar Y, Hoffman E et al. Comparison of systolic blood pressure values obtained by photoplethysmography and by Korotkoff sounds[J]. Sensors, 13, 14797-14812(2013).

    [23] Daly S M, Leahy M J. ‘Go with the flow’: a review of methods and advancements in blood flow imaging[J]. Journal of Biophotonics, 6, 217-255(2013).

    [24] Teplov V, Nippolainen E, Makarenko A A et al. Ambiguity of mapping the relative phase of blood pulsations[J]. Biomedical Optics Express, 5, 3123-3139(2014).

    [25] Sidorov I S, Romashko R V, Koval V T et al. Origin of infrared light modulation in reflectance-mode photoplethysmography[J]. PLoS One, 11, e0165413(2016).

    [26] Dong A Y, Honnorat N, Gaonkar B et al. CHIMERA: clustering of heterogeneous disease effects via distribution matching of imaging patterns[J]. IEEE Transactions on Medical Imaging, 35, 612-621(2016).

    [27] Varol E, Sotiras A, Davatzikos C. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework[J]. NeuroImage, 145, 346-364(2017).

    [28] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C](2003).

    [29] Baltrusaitis T, Robinson P, Morency L P. Constrained local neural fields for robust facial landmark detection in the wild[C], 354-361(2014).

    [30] Baltrušaitis T, Robinson P, Morency L P. 3D constrained local model for rigid and non-rigid facial tracking[C], 2610-2617(2012).

    [31] Henriques J F, Caseiro R, Martins P et al. Exploiting the circulant structure of tracking-by-detection with kernels[M]. Fitzgibbon A, Lazebnik S, Perona P, et al. Computer vision-ECCV 2012. Lecture notes in computer science, 7575, 702-715(2012).

    [32] Li X B, Chen J, Zhao G Y et al. Remote heart rate measurement from face videos under realistic situations[C], 4264-4271(2014).

    [33] Liu L, Dong H W, Tong J. Video-based heart rate measuring method[J]. Computer Engineering and Applications, 51, 199-203(2015).

    [34] Bal U. Non-contact estimation of heart rate and oxygen saturation using ambient light[J]. Biomedical Optics Express, 6, 86-97(2015).

    [35] Wang W J, Stuijk S, de Haan G. Unsupervised subject detection via remote PPG[J]. IEEE Transactions on Biomedical Engineering, 62, 2629-2637(2015).

    [36] Wang W J, Stuijk S, de Haan G. Living-skin classification via remote-PPG[J]. IEEE Transactions on Biomedical Engineering, 64, 2781-2792(2017).

    [37] Liu H, Chen T, Zhang Q N et al. A new approach for face detection based on photoplethysmographic imaging[M]. Yin X X, Ho K, Zeng D, et al. Health information science. Lecture notes in computer science, 9085, 79-91(2015).

    [38] Tarassenko L, Villarroel M, Guazzi A et al. Non-contact video-based vital sign monitoring using ambient light and auto-regressive models[J]. Physiological Measurement, 35, 807-831(2014).

    [39] Lin L, Zhang B W, Wang J X et al. Research advance of cognitive reserve in brain aging[J]. Chinese Medical Equipment Journal, 38, 93-98(2017).

    [40] Budd Haeberlein S, O'Gorman J, Chiao P et al. Clinical development of aducanumab, an anti-aβ human monoclonal antibody being investigated for the treatment of early Alzheimer’s disease[J]. The Journal of Prevention of Alzheimer’s Disease, 4, 255-263(2017).

    [41] Poh M Z, McDuff D J, Picard R W. Advancements in noncontact, multiparameter physiological measurements using a webcam[J]. IEEE Transactions on Biomedical Engineering, 58, 7-11(2011).

    [42] Lewandowska M, Rumiński J, Kocejko T et al. Measuring pulse rate with a webcam: a non-contact method for evaluating cardiac activity[C], 405-410(2011).

    [43] Wang W J, Stuijk S, de Haan G. A novel algorithm for remote photoplethysmography: spatial subspace rotation[J]. IEEE Transactions on Biomedical Engineering, 63, 1974-1984(2016).

    [44] Wang W J, den Brinker A C, Stuijk S et al. Algorithmic principles of remote PPG[J]. IEEE Transactions on Bio-Medical Engineering, 64, 1479-1491(2017).

    [45] Lee H, Ko H, Chung H et al. Robot assisted instantaneous heart rate estimator using camera based remote photoplethysmograpy via plane-orthogonal-to-skin and finite state machine[C], 4425-4428(2020).

    [46] De Haan G, Jeanne V. Robust pulse rate from chrominance-based rPPG[J]. IEEE Transactions on Biomedical Engineering, 60, 2878-2886(2013).

    [47] de Haan G, van Leest A. Improved motion robustness of remote-PPG by using the blood volume pulse signature[J]. Physiological Measurement, 35, 1913-1926(2014).

    [48] Unakafov A M, Möller S, Kagan I et al. Using imaging photoplethysmography for heart rate estimation in non-human Primates[J]. PLoS One, 13, e0202581(2018).

    [49] Wang W J, den Brinker A C, Stuijk S et al. Robust heart rate from fitness videos[J]. Physiological Measurement, 38, 1023-1044(2017).

    [50] Wang W J, den Brinker A C, Stuijk S et al. Color-distortion filtering for remote photoplethysmography[C], 71-78(2017).

    [51] Zhang F, Chen S X, Zhang H S et al. Bioelectric signal detrending using smoothness prior approach[J]. Medical Engineering & Physics, 36, 1007-1013(2014).

    [52] Bousefsaf F, Maaoui C, Pruski A. Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate[J]. Biomedical Signal Processing and Control, 8, 568-574(2013).

    [53] Holton B D, Mannapperuma K, Lesniewski P J et al. Signal recovery in imaging photoplethysmography[J]. Physiological Measurement, 34, 1499-1511(2013).

    [54] Favilla R, Zuccalà V C, Coppini G. Heart rate and heart rate variability from single-channel video and ICA integration of multiple signals[J]. IEEE Journal of Biomedical and Health Informatics, 23, 2398-2408(2019).

    [55] Siddiqui S A, Zhang Y, Feng Z Q et al. A pulse rate estimation algorithm using PPG and smartphone camera[J]. Journal of Medical Systems, 40, 126(2016).

    [56] Amelard R, Clausi D A, Wong A. Spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging[J]. Biomedical Optics Express, 7, 4874-4885(2016).

    [57] Laure D, Paramonov I. Improved algorithm for heart rate measurement using mobile phone camera[C], 85-93(2013).

    [58] Yu Y P, Raveendran P, Lim C L. Heart rate estimation from facial images using filter bank[C], 69-72(2014).

    [59] Kumar M, Veeraraghavan A, Sabharwal A. DistancePPG: robust non-contact vital signs monitoring using a camera[J]. Biomedical Optics Express, 6, 1565-1588(2015).

    [60] Wang D L, Yang X Z, Liu X N et al. Detail-preserving pulse wave extraction from facial videos using consumer-level camera[J]. Biomedical Optics Express, 11, 1876-1891(2020).

    [61] Villarroel M, Guazzi A, Jorge J et al. Continuous non-contact vital sign monitoring in neonatal intensive care unit[J]. Healthcare Technology Letters, 1, 87-91(2014).

    [62] Jayadevappa B M, Holi M. An estimation technique using FFT for heart rate derived from PPG signal[J]. Global Journals of Research in Engineering, 15, 45-51(2015).

    [63] Kong L Q, Wu Y H, Zhao Y J et al. Robust imaging photoplethysmography in long-distance motion[J]. IEEE Photonics Journal, 12, 3900512(2020).

    [64] Chen D S, Liu Z K. A survey of skin color detection[J]. Chinese Journal of Computers, 29, 194-207(2006).

    [65] Cheng C, Da F P, Wang C X et al. Pose invariant face recognition using maximum Gabor similarity based on Lucas-Kanade algorithm[J]. Acta Optica Sinica, 39, 0715005(2019).

    [66] Meng J, Zhao X M. Human body recognition and positioning with multiple cameras based on “vibration signals” from skin surfaces[J]. Acta Optica Sinica, 39, 0515001(2019).

    [67] Gibert G, D'Alessandro D, Lance F. Face detection method based on photoplethysmography[C], 449-453(2013).

    [68] Ren X, Malik J. Learning a classification model for segmentation[C], 10-17(2008).

    [69] Mori G. Guiding model search using segmentation[C], 1417-1423(2005).

    [70] Levinshtein A, Stere A, Kutulakos K N et al. TurboPixels: fast superpixels using geometric flows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2290-2297(2009).

    [71] Vedaldi A, Soatto S. Quick shift and kernel methods for mode seeking[M]. Forsyth D, Torr P, Zisserman Z. Computer vision-ECCV 2008. Lecture notes in computer science, 5305, 705-718(2008).

    [72] Achanta R, Shaji A, Smith K et al. SLIC superpixels[J]. EPFL Technical Report, 149300(2010).

    [73] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012).

    [74] Kong L Q, Wu Y H, Zhao Y J et al. IPPG alive-skin detection based on superpixel segmentation[J]. Acta Optica Sinica, 40, 1310001(2020).

    [75] Yin C, Li W X. Application of photoelectric pulse meter in blood oxygen saturation measurement[J]. Wireless Internet Technology, 19, 106-108(2022).

    [76] Haacke E M, Lai S, Reichenbach J R et al. In vivo measurement of blood oxygen saturation using magnetic resonance imaging: a direct validation of the blood oxygen level-dependent concept in functional brain imaging[J]. Human Brain Mapping, 5, 341-346(1997).

    [77] Shao D D, Liu C B, Tsow F et al. Noncontact monitoring of blood oxygen saturation using camera and dual-wavelength imaging system[J]. IEEE Transactions on Biomedical Engineering, 63, 1091-1098(2016).

    [78] Humphreys K, Ward T, Markham C. Noncontact simultaneous dual wavelength photoplethysmography: a further step toward noncontact pulse oximetry[J]. Review of Scientific Instruments, 78, 044304(2007).

    [79] Fei J, Pavlidis I. Thermistor at a distance: unobtrusive measurement of breathing[J]. IEEE Transactions on Biomedical Engineering, 57, 988-998(2010).

    [80] Gioux S, Stockdale A, Oketokoun R et al. First-in-human pilot study of a spatial frequency domain oxygenation imaging system[J]. Journal of Biomedical Optics, 16, 086015(2011).

    [81] Humphreys K, Ward T, Markham C. A CMOS camera-based pulse oximetry imaging system[C], 3494-3497(2006).

    [82] Guazzi A R, Villarroel M, Jorge J et al. Non-contact measurement of oxygen saturation with an RGB camera[J]. Biomedical Optics Express, 6, 3320-3338(2015).

    [83] Tsai H Y, Huang K C, Chang H C et al. A noncontact skin oxygen-saturation imaging system for measuring human tissue oxygen saturation[J]. IEEE Transactions on Instrumentation and Measurement, 63, 2620-2631(2014).

    [84] Harford M, Catherall J, Gerry S et al. Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review[J]. Physiological measurement, 40, 06TR01(2019).

    [85] Wieringa F P, Mastik F, van der Steen A F W. Contactless multiple wavelength photoplethysmographic imaging: a first step toward “SpO2 camera” technology[J]. Annals of Biomedical Engineering, 33, 1034-1041(2005).

    [86] Wieringa F P, Mastik F, Boks R H et al. In vitro demonstration of an SpO2- camera[C], 749-751(2009).

    [87] Al-Naji A, Khalid G A, Mahdi J F et al. Non-contact SpO2 prediction system based on a digital camera[J]. Applied Sciences, 11, 4255(2021).

    [88] Li J, Dunmire B, Beach K W et al. A reflectance model for non-contact mapping of venous oxygen saturation using a CCD camera[J]. Optics Communications, 308, 78-84(2013).

    [89] Kong L Q, Zhao Y J, Dong L Q et al. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light[J]. Optics Express, 21, 17464-17471(2013).

    [90] Leung A[M]. Multimedia, communication and computing application, 526(2015).

    [91] Fu R R, Wang H. Detection of driving fatigue by using noncontact EMG and ECG signals measurement system[J]. International Journal of Neural Systems, 24, 1450006(2014).

    [92] Miyajima C, Nishiwaki Y, Ozawa K et al. Driver modeling based on driving behavior and its evaluation in driver identification[J]. Proceedings of the IEEE, 95, 427-437(2007).

    [93] Pilutti T, Ulsoy G. On-line identification of driver state for lane-keeping tasks[C], 678-681(2002).

    [94] Zhang X B, Cheng B, Feng R J. Real-time detection of driver drowsiness based on steering performance[J]. Journal of Tsinghua University (Science and Technology), 50, 1072-1076, 1081(2010).

    [95] Grace R, Byrne V E, Bierman D M et al. A drowsy driver detection system for heavy vehicles[C], I36/1-I36/8(2002).

    [96] Choi I H, Kim Y G. Head pose and gaze direction tracking for detecting a drowsy driver[C], 241-244(2014).

    [97] Wang H L, Liu H H, Song Z M. Fatigue driving detection system design based on driving behavior[C], 549-552(2011).

    [98] Abtahi S, Hariri B, Shirmohammadi S. Driver drowsiness monitoring based on yawning detection[C](2011).

    [99] Putta R, Shinde G N, Lohani P. Real time drowsiness detection system using viola Jones algorithm[J]. International Journal of Computer Applications, 95, 28-34(2014).

    [100] Lu Y F, Wang Z C. Detecting driver yawning in successive images[C], 581-583(2007).

    [101] Sabet M, Zoroofi R A, Sadeghniiat-Haghighi K et al. A new system for driver drowsiness and distraction detection[C], 1247-1251(2012).

    [102] Baharav A, Kotagal S, Gibbons V et al. Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability[J]. Neurology, 45, 1183-1187(1995).

    [103] Furman G D, Baharav A, Cahan C et al. Early detection of falling asleep at the wheel: a Heart Rate Variability approach[C], 1109-1112(2009).

    [104] Li G, Chung W Y. Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier[J]. Sensors, 13, 16494-16511(2013).

    [105] Roman B, Pavel S, Miroslav P et al. Fatigue indicators of drowsy drivers based on analysis of physiological signals[M]. Crespo J, Maojo V, Martin F, et al. Medical data analysis. Lecture notes in computer science, 2199, 62-68(2001).

    [106] Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review[J]. Sensors, 12, 16937-16953(2012).

    [107] Abe E, Fujiwara K, Hiraoka T et al. Development of drowsy driving accident prediction by heart rate variability analysis[C](2015).

    [108] Michail E, Kokonozi A, Chouvarda I et al. EEG and HRV markers of sleepiness and loss of control during car driving[C], 2566-2569(2008).

    [109] Jung S J, Shin H S, Chung W Y. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel[J]. IET Intelligent Transport Systems, 8, 43-50(2014).

    [110] Shin H S, Jung S J, Kim J J et al. Real time car driver’s condition monitoring system[C], 951-954(2011).

    [111] Beck A, Steer R, Brown G. Manual for the beck depression inventory-II[J]. Psychological Corporation, 21, 1-9(1996).

    [112] Davidson J R, Book S W, Colket J T et al. Assessment of a new self-rating scale for post-traumatic stress disorder[J]. Psychological Medicine, 27, 153-160(1997).

    [113] Lavoie J A A, Douglas K S. The perceived stress scale: evaluating configural, metric and scalar invariance across mental health status and gender[J]. Journal of Psychopathology and Behavioral Assessment, 34, 48-57(2012).

    [114] Lee E H. Review of the psychometric evidence of the perceived stress scale[J]. Asian Nursing Research, 6, 121-127(2012).

    [115] Zhai J, Barreto A. Stress detection in computer users based on digital signal processing of noninvasive physiological variables[C], 1355-1358(2016).

    [116] Hernandez J, Morris R R, Picard R W. Call center stress recognition with person-specific models[M]. D’Mello S, Graesser A, Schuller B, et al. Affective computing and intelligent interaction. Lecture notes in computer science, 6974, 125-134(2011).

    [117] Tao L. The review of research on post-graduate stress in China in the past 20 years[J]. Journal of University of Science and Technology Beijing (Social Sciences Edition), 36, 36-42(2020).

    [118] Chen T, Yuen P, Richardson M et al. Detection of psychological stress using a hyperspectral imaging technique[J]. IEEE Transactions on Affective Computing, 5, 391-405(2014).

    [119] Schützwohl A, Reisenzein R. Facial expressions in response to a highly surprising event exceeding the field of vision: a test of Darwin’s theory of surprise[J]. Evolution and Human Behavior, 33, 657-664(2012).

    [120] Lazarus R S. From psychological stress to the emotions: a history of changing outlooks[J]. Annual Review of Psychology, 44, 1-22(1993).

    [121] Little A C, McPherson J, Dennington L et al. Accuracy in assessment of self-reported stress and a measure of health from static facial information[J]. Personality and Individual Differences, 51, 693-698(2011).

    [122] Kong L Q, Chen F, Zhao Y J et al. Non-contact psychological stress detection combining heart rate variability and facial expressions[J]. Acta Optica Sinica, 41, 0310003(2021).

    [123] Pilt K, Meigas K, Ferenets R et al. Photoplethysmographic signal waveform index for detection of increased arterial stiffness[J]. Physiological Measurement, 35, 2027-2036(2014).

    [124] Schönauer M, Thomas A, Morbach S et al. Cardiac autonomic diabetic neuropathy[J]. Diabetes and Vascular Disease Research, 5, 336-344(2008).

    [125] He X C, Goubran R A, Liu X P. Secondary peak detection of PPG signal for continuous cuffless arterial blood pressure measurement[J]. IEEE Transactions on Instrumentation and Measurement, 63, 1431-1439(2014).

    [126] Liu W C, Fang X, Chen Q Q et al. Reliability analysis of an integrated device of ECG, PPG and pressure pulse wave for cardiovascular disease[J]. Microelectronics Reliability, 87, 183-187(2018).

    [127] Lilia C M, Gerson O S, Alvaro M O et al. Endothelial dysfunction evaluated using photoplethysmography in patients with type 2 diabetes[J]. Journal of Cardiovascular Diseases & Diagnosis, 3, 1-7(2015).

    [128] Gibbons G W, Shaw P M. Diabetic vascular disease: characteristics of vascular disease unique to the diabetic patient[J]. Seminars in Vascular Surgery, 25, 89-92(2012).

    [129] Nesto R W, Rutter M K. Impact of the atherosclerotic process in patients with diabetes[J]. Acta Diabetologica, 39, S22-S28(2002).

    [130] Weber T, O'Rourke M F, Lassnig E et al. Pulse waveform characteristics predict cardiovascular events and mortality in patients undergoing coronary angiography[J]. Journal of Hypertension, 28, 797-805(2010).

    [131] Avolio A P, Butlin M, Walsh A. Arterial blood pressure measurement and pulse wave analysis: their role in enhancing cardiovascular assessment[J]. Physiological Measurement, 31, R1-R47(2010).

    [132] Yokoyama H, Shoji T, Kimoto E et al. Pulse wave velocity in lower-limb arteries among diabetic patients with peripheral arterial disease[J]. Journal of Atherosclerosis and Thrombosis, 10, 253-258(2003).

    [133] Xu G, Dong L Q, Yuan J et al. Rational selection of RGB channels for disease classification based on IPPG technology[J]. Biomedical Optics Express, 13, 1820-1833(2022).

    Lingqin Kong, Yuejin Zhao, Liquan Dong, Ming Liu, Ge Xu, Mei Hui, Xuhong Chu. Non-Contact Physiological Parameter Detection and Its Application Based on Imaging Photoplethysmography[J]. Acta Optica Sinica, 2023, 43(15): 1512002
    Download Citation