{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:28Z","timestamp":1760146588585,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, ROC","award":["113-2221-E-230-004"],"award-info":[{"award-number":["113-2221-E-230-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the \u201csilent killer\u201d nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study compares two distinct non-contact BP measurement approaches: one combining the Nelder\u2013Mead simplex method with particle swarm optimization (NM-PSO) and the other using machine learning regression analysis. In the NM-PSO method, a standard webcam captures continuous images of the palm, extracting physiological data through light wave reflection and employing independent component analysis (ICA) to remove noise artifacts. The NM-PSO achieves a verified root mean square error (RMSE) of 2.71 mmHg for systolic blood pressure (SBP) and 3.42 mmHg for diastolic blood pressure (DBP). Alternatively, the regression method derives BP values through machine learning-based regression formulas, resulting in an RMSE of 2.88 mmHg for SBP and 2.60 mmHg for DBP. Both methods enable fast, accurate, and convenient BP measurement within 10 s, suitable for home use. This study demonstrates a cost-effective solution for non-contact BP monitoring and highlights each method\u2019s advantages. The NM-PSO approach emphasizes optimization in noise handling, while the regression method leverages formulaic efficiency in BP estimation. These results offer a biomimetic approach that could replace traditional contact-based BP measurement devices, contributing to enhanced accessibility in hypertension management.<\/jats:p>","DOI":"10.3390\/biomimetics9110713","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:14:38Z","timestamp":1732094078000},"page":"713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Real-Time Palm Imaging with Nelder\u2013Mead Particle Swarm Optimization\/Regression Algorithms for Non-Contact Blood Pressure Detection"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7241-0576","authenticated-orcid":false,"given":"Te-Jen","family":"Su","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Chung","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Hong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2844-6177","authenticated-orcid":false,"given":"Wen-Rong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6411-7293","authenticated-orcid":false,"given":"Qian-Yi","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan-Xiang","family":"Fei","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung 807618, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8666-7687","authenticated-orcid":false,"given":"Shih-Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Cheng Shiu University, Kaohsiung 833301, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Esmaelpoor, J., Moradi, M.H., and Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Comput. Biol. Med., 120.","DOI":"10.1016\/j.compbiomed.2020.103719"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aguirre, N., Grall-Ma\u00ebs, E., Cymberknop, L.J., and Armentano, R.L. (2021). Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism. Sensors, 21.","DOI":"10.3390\/s21062167"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/JSEN.2022.3230210","article-title":"Hybrid D1DCnet Using Forehead iPPG for Continuous and Noncontact Blood Pressure Measurement","volume":"23","author":"Li","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"437","DOI":"10.3390\/biomedinformatics4010025","article-title":"Non-Contact Blood Pressure Estimation Using Forehead and Palm Infrared Video","volume":"4","author":"Stogiannopoulos","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1364\/BOE.514241","article-title":"Fair non-contact blood pressure estimation using imaging photoplethysmography","volume":"15","author":"Fang","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cheng, H., Xiong, J., Chen, Z., and Chen, J. (2023). Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research. Sensors, 23.","DOI":"10.3390\/s23125528"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Baker, S., Xiang, W., and Atkinson, I. (2021). A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms. Comput. Methods Programs Biomed., 207.","DOI":"10.1016\/j.cmpb.2021.106191"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Goudarz, R.H., Mousavi, S.S., and Charmi, M. (2021, January 28\u201329). Introducing a New Feature Extraction Method for Non-Contact Blood Pressure Estimating Through iPPG Signals Extracted using G-R Method from Video Images of Different Facial Regions. Proceedings of the 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), Kashan, Iran.","DOI":"10.1109\/IPRIA53572.2021.9483529"},{"key":"ref_9","unstructured":"Harris, M., and Agoes, A.S. (2021, January 12). Applying hand gesture recognition for user guide application using MediaPipe. Proceedings of the 2nd International Seminar of Science and Applied Technology (ISSAT 2021), Online."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Veluri, R.K., Sree, S.R., Vanathi, A., Aparna, G., and Vaidya, S.P. (2021, January 28\u201329). Hand gesture mapping using MediaPipe algorithm. Proceedings of the Third International Conference on Communication, Computing and Electronics Systems: ICCCES 2021, Coimbatore, India.","DOI":"10.1007\/978-981-16-8862-1_39"},{"key":"ref_11","first-page":"462","article-title":"A study on real-time hand gesture recognition technology by machine learning-based mediapipe","volume":"12","author":"Han","year":"2022","journal-title":"J. Syst. Manag. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Song, R., Zhang, S., Cheng, J., Li, C., and Chen, X. (2020). New insights on super-high resolution for video-based heart rate estimation with a semi-blind source separation method. Comput. Biol. Med., 116.","DOI":"10.1016\/j.compbiomed.2019.103535"},{"key":"ref_13","unstructured":"Lee, Y.-C. (2022). Measurements of Heart Rate and Heart Rate Variability with Realtime Images Based on Independent Component Analysis and Particle Swarm Optimization, Electronic Engineering. [Master\u2019s Thesis, National Kaohsiung University of Science and Technology]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120451","DOI":"10.1016\/j.talanta.2019.120451","article-title":"Independent components analysis (ICA) at the \u201ccocktail-party\u201d in analytical chemistry","volume":"208","author":"Monakhova","year":"2020","journal-title":"Talanta"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A simplex method for function minimization","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124623","DOI":"10.1016\/j.energy.2022.124623","article-title":"Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser","volume":"256","author":"Neshat","year":"2022","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10589-010-9329-3","article-title":"Implementing the Nelder-Mead simplex algorithm with adaptive parameters","volume":"51","author":"Gao","year":"2012","journal-title":"Comput. Optim. Appl."},{"key":"ref_18","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, Australia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","article-title":"Particle Swarm Optimization: A Comprehensive Survey","volume":"10","author":"Shami","year":"2022","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: An overview","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Comput."},{"key":"ref_21","first-page":"263","article-title":"Solving multi-objective syngas optimization problems using a hybrid evolutionary algorithm","volume":"3","author":"Gao","year":"2014","journal-title":"J. Manag. Inf. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"140","DOI":"10.38094\/jastt1457","article-title":"A review on linear regression comprehensive in machine learning","volume":"1","author":"Maulud","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hope, T.M.H. (2020). Linear regression. Machine Learning, Academic Press.","DOI":"10.1016\/B978-0-12-815739-8.00004-3"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R., and Taylor, J. (2023). Linear regression. An Introduction to Statistical Learning: With Applications in Python, Springer International Publishing.","DOI":"10.1007\/978-3-031-38747-0"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110080","DOI":"10.1016\/j.measurement.2021.110080","article-title":"Etemadi multiple linear regression","volume":"186","author":"Etemadi","year":"2021","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1213\/ANE.0000000000005206","article-title":"Linear regression in medical research","volume":"132","author":"Schober","year":"2021","journal-title":"Anesth. Analg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1109\/TBME.2013.2266196","article-title":"Robust Pulse Rate From Chrominance-Based rPPG","volume":"60","author":"Jeanne","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16729","DOI":"10.1007\/s12652-023-04683-w","article-title":"Analysis of vital signs using remote photoplethysmography (RPPG)","volume":"14","author":"Karthick","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6904","DOI":"10.3758\/s13428-024-02398-0","article-title":"Remote photoplethysmography (rPPG) in the wild: Remote heart rate imaging via online webcams","volume":"56","author":"Finotti","year":"2024","journal-title":"Behav. Res. Methods"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lee, H., Cho, A., and Whang, M. (2021). Fusion Method to Estimate Heart Rate from Facial Videos Based on RPPG and RBCG. Sensors, 21.","DOI":"10.3390\/s21206764"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Benedetto, S., Caldato, C., Greenwood, D.C., Bartoli, N., Pensabene, V., and Actis, P. (2019). Remote heart rate monitoring-Assessment of the Facereader rPPg by Noldus. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0225592"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kossack, B., Wisotzky, E., Eisert, P., Schraven, S.P., Globke, B., and Hilsmann, A. (2022, January 18\u201324). Perfusion assessment via local remote photoplethysmography (rPPG). Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00238"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1002\/cphc.202000464","article-title":"The Bouguer-Beer-Lambert law: Shining light on the obscure","volume":"21","author":"Pahlow","year":"2020","journal-title":"ChemPhysChem"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.bspc.2012.10.004","article-title":"Resonance phenomenon during wrist pulse-taking: A stochastic simulation, model-based study of the \u2018pressing with one finger\u2019technique","volume":"8","year":"2013","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lumyong, C., Yodrabum, N., Winaikosol, K., and Titijaroonroj, T. (2023, January 21\u201324). Skin Video-based Blood Pressure Approximation Using CHROM with LSTM-NN. Proceedings of the 2023 15th International Conference on Knowledge and Smart Technology (KST), Phuket, Thailand.","DOI":"10.1109\/KST57286.2023.10086816"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1007\/s10237-023-01735-0","article-title":"Turbulent blood dynamics in the left heart in the presence of mitral regurgitation: A computational study based on multi-series cine-MRI","volume":"22","author":"Bennati","year":"2023","journal-title":"Biomech. Model. Mechanobiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bennati, L., Giambruno, V., Renzi, F., Di Nicola, V., Maffeis, C., Puppini, G., Luciani, G.B., and Vergara, C. (2023). Turbulence and blood washout in presence of mitral regurgitation: A computational fluid-dynamics study in the complete left heart. bioRxiv, 3.","DOI":"10.1101\/2023.03.19.533094"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40430-020-02608-8","article-title":"Computational studies of comparative and cumulative effects of turbulence, fluid\u2013structure interactions, and uniform magnetic fields on pulsatile non-Newtonian flow in a patient-specific carotid artery","volume":"42","author":"Moradicheghamahi","year":"2020","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_39","first-page":"011002","article-title":"Pulsatile aortic blood flow\u2014A critical assessment of boundary conditions","volume":"4","author":"Fuchs","year":"2021","journal-title":"J. Eng. Sci. Med. Diagn. Ther."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1007\/s12652-018-1026-6","article-title":"Robust blood pressure estimation using an RGB camera","volume":"11","author":"Fan","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10624","DOI":"10.1109\/JSEN.2019.2931775","article-title":"The Noninvasive Blood Pressure Measurement Based on Facial Images Processing","volume":"19","author":"Zhou","year":"2019","journal-title":"IEEE Sens. J."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/9\/11\/713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:35:48Z","timestamp":1760114148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/9\/11\/713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":41,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["biomimetics9110713"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics9110713","relation":{},"ISSN":["2313-7673"],"issn-type":[{"type":"electronic","value":"2313-7673"}],"subject":[],"published":{"date-parts":[[2024,11,20]]}}}