{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:12:07Z","timestamp":1775578327488,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,3]],"date-time":"2018-06-03T00:00:00Z","timestamp":1527984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["IH120100053"],"award-info":[{"award-number":["IH120100053"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks). To validate the proposed method and ML models, a home oscillometric monitor and a finger sensor were used. Results showed high correlations with the G color channel (R2 = 0.83). Two ML models were developed using three face-regions: (i) Model 1 to predict HR and BP using the RVA outputs with R = 0.85 and (ii) Model 2 based on time-series prediction with HR, magnitude and luminosity from RVA inputs to HR values every second with R = 0.97. An application for the sensory analysis of chocolate showed significant correlations between changes in HR and BP with chocolate hardness and purchase intention.<\/jats:p>","DOI":"10.3390\/s18061802","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T08:59:41Z","timestamp":1528102781000},"page":"1802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-9307","authenticated-orcid":false,"given":"Claudia","family":"Gonzalez Viejo","sequence":"first","affiliation":[{"name":"School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-5085","authenticated-orcid":false,"given":"Sigfredo","family":"Fuentes","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"given":"Damir D.","family":"Torrico","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3998-1240","authenticated-orcid":false,"given":"Frank R.","family":"Dunshea","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,3]]},"reference":[{"key":"ref_1","unstructured":"Ahrens, T., Rutherford, K., and Basham, K.A.R. (1993). Essentials of Oxygenation: Implication for Clinical Practice, Jones and Bartlett Publishers."},{"key":"ref_2","first-page":"39","article-title":"Evaluation of Electrocardiogram for Biometric Authentication","volume":"3","author":"Singh","year":"2012","journal-title":"J. Inf. Secur."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.biopsycho.2010.03.010","article-title":"Autonomic nervous system activity in emotion: A review","volume":"84","author":"Kreibig","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.foodqual.2017.11.010","article-title":"Images and chocolate stimuli affect physiological and affective responses of consumers: A cross-cultural study","volume":"65","author":"Torrico","year":"2018","journal-title":"Food Qual. Preference"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gonzalez Viejo, C., Fuentes, S., Howell, K., Torrico, D.D., and Dunshea, F.R. (2018). Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. Physiol. Behav., in press.","DOI":"10.1016\/j.physbeh.2018.02.051"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.foodcont.2018.04.037","article-title":"Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications","volume":"92","author":"Viejo","year":"2018","journal-title":"Food Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.medengphy.2006.09.006","article-title":"Heart rate measurement based on a time-lapse image","volume":"29","author":"Takano","year":"2007","journal-title":"Med. Eng. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1109\/TIFS.2012.2215324","article-title":"ECG biometric recognition: A comparative analysis","volume":"7","author":"Odinaka","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1056\/NEJMra060433","article-title":"Ambulatory blood-pressure monitoring","volume":"354","author":"Pickering","year":"2006","journal-title":"N. Engl. J. Med."},{"key":"ref_10","unstructured":"Heyward, V.H. (2008). Evaluaci\u00f3n de la Aptitud F\u00edsica y Prescripci\u00f3n del Ejercicio, Editorial Medica Panamericana Sa de."},{"key":"ref_11","first-page":"877","article-title":"Ambulatory blood pressure monitoring","volume":"40","author":"Head","year":"2011","journal-title":"Aust. Fam. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Flynn, J., Ingelfinger, J.R., and Portman, R.J. (2013). Pediatric Hypertension, Humana Press.","DOI":"10.1007\/978-1-62703-490-6"},{"key":"ref_13","first-page":"148","article-title":"An Overview on Heart Rate Monitoring and Pulse Oximeter System","volume":"3","author":"Jahan","year":"2014","journal-title":"Int. J. Latest Res. Sci. Technol."},{"key":"ref_14","unstructured":"Yurish, S. (2018). Advances in Optics, Ifsa Publishing."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jain, M., Deb, S., and Subramanyam, A. (2016, January 21\u201323). Face video based touchless blood pressure and heart rate estimation. Proceedings of the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada.","DOI":"10.1109\/MMSP.2016.7813389"},{"key":"ref_16","first-page":"6593","article-title":"Analysis of Heart Rate Monitoring Using a Webcam","volume":"3","author":"Carvalho","year":"2014","journal-title":"Analysis"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J.V., Durand, F., and Freeman, W.T. (2012). Eulerian Video Magnification for Revealing Subtle Changes in the World, Association for Computing Machinery.","DOI":"10.1145\/2185520.2335416"},{"key":"ref_18","unstructured":"Jensen, J.N., and Hannemose, M. (2014). Camera-Based Heart Rate Monitoring, Technical University of Denmark, Department of Applied Mathematics and Computer Science, DTU Computer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1757001","DOI":"10.1142\/S0218001417570014","article-title":"Noncontact heart activity measurement system based on video imaging analysis","volume":"31","author":"Chahl","year":"2017","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1080\/03091902.2017.1313326","article-title":"Remote sensing of physiological signs using a machine vision system","volume":"41","author":"Gibson","year":"2017","journal-title":"J. Med. Eng. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wei, L., Tian, Y., Wang, Y., Ebrahimi, T., and Huang, T. (2012, January 5\u20136). Automatic webcam-based human heart rate measurements using laplacian eigenmap. Proceedings of the Computer Vision\u2013ACCV, Daejeon, Korea.","DOI":"10.1007\/978-3-642-37444-9_22"},{"key":"ref_22","unstructured":"Lewandowska, M., Rumi\u0144ski, J., Kocejko, T., and Nowak, J. (2011, January 18\u201321). Measuring pulse rate with a webcam\u2014A non-contact method for evaluating cardiac activity. Proceedings of the 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), Szczecin, Poland."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tasli, H.E., Gudi, A., and den Uyl, M. (2014, January 27\u201330). Remote PPG based vital sign measurement using adaptive facial regions. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025282"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.measurement.2016.11.039","article-title":"Evaluation of psychological effects on human postural stability","volume":"98","author":"Frelih","year":"2017","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.foodqual.2012.04.015","article-title":"Autonomic nervous system responses on and facial expressions to the sight, smell, and taste of liked and disliked foods","volume":"26","author":"Kooijman","year":"2012","journal-title":"Food Qual. Preference"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1126\/science.6612338","article-title":"Autonomic nervous system activity distinguishes among emotions","volume":"221","author":"Ekman","year":"1983","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1037\/0022-3514.62.6.972","article-title":"Emotion and autonomic nervous system activity in the Minangkabau of West Sumatra","volume":"62","author":"Levenson","year":"1992","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/j.1469-8986.1993.tb03354.x","article-title":"The psychophysiology of disgust: Differentiating negative emotional contexts with facial EMG","volume":"30","author":"Vrana","year":"1993","journal-title":"Psychophysiology"},{"key":"ref_29","first-page":"1672","article-title":"Using noninvasive wearable computers to recognize human emotions from physiological signals","volume":"2004","author":"Lisetti","year":"2004","journal-title":"EURASIP J. Appl. Signal Process."},{"key":"ref_30","unstructured":"Heuer, A., and Scanlan, C.L. (2013). Wilkins\u2019 Clinical Assessment in Respiratory Care\u2014E-Book, Elsevier Health Sciences."},{"key":"ref_31","first-page":"653625","article-title":"Importance of continuous pulse oximetry of the ipsilateral thumb\/index finger during transradial angiography","volume":"2011","author":"Puffer","year":"2011","journal-title":"Case Rep. Anesthesiol."},{"key":"ref_32","unstructured":"Mathworks Inc. (2018, March 26). Ground Truth Labeler. Available online: https:\/\/au.mathworks.com\/help\/vision\/examples\/object-detection-in-a-cluttered-scene-using-point-feature-matching.html."},{"key":"ref_33","unstructured":"MathWorks Inc. (2018, March 24). Findpeaks, Find Local Maxima. Available online: https:\/\/au.mathworks.com\/help\/signal\/ref\/findpeaks.html."},{"key":"ref_34","unstructured":"Rovai, A.P., Baker, J.D., and Ponton, M.K. (2013). Social Science Research Design and Statistics: A Practitioner\u2019s Guide to Research Methods and IBM SPSS, Watertree Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cichosz, P. (2015). Data Mining Algorithms: Explained Using R, Wiley.","DOI":"10.1002\/9781118950951"},{"key":"ref_36","first-page":"30","article-title":"JMASM 49: A Compilation of Some Popular Goodness of Fit Tests for Normal Distribution: Their Algorithms and MATLAB Codes (MATLAB)","volume":"16","year":"2017","journal-title":"J. Mod. Appl. Statist. Methods"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0031-3203(02)00063-8","article-title":"Classification of heart rate data using artificial neural network and fuzzy equivalence relation","volume":"36","author":"Acharya","year":"2003","journal-title":"Pattern Recogn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"289","DOI":"10.4236\/jbise.2011.44039","article-title":"Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network","volume":"4","author":"Gothwal","year":"2011","journal-title":"J. Biomed. Sci. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1802\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:07:05Z","timestamp":1760195225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1802"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,3]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["s18061802"],"URL":"https:\/\/doi.org\/10.3390\/s18061802","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,3]]}}}