{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T07:09:11Z","timestamp":1779088151539,"version":"3.51.4"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universitat de Valencia"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Remote photoplethysmography (rPPG) has gained popularity as a non-invasive technique for remote monitoring, as it can provide accurate measurements of an individual\u2019s physiological signals under controlled conditions. However, the accuracy of rPPG can be affected by various factors, such as movement artifacts, changes in skin tone, and the presence of other sources of light in the environment. To improve the reliability of rPPG measurements in real-world monitoring settings and reduce the frequency of false alarms in health monitoring settings, we propose a confidence score indicating the quality of the predictions. This score was built by identifying meaningful variables related to motion that strongly correlate with the accuracy of the measurements and training a classifier with data coming from 3 distinct datasets, to improve the model\u2019s robustness, reproducibility, and generalizability. Despite that only motion-related features have been considered, the high AUC values obtained in all cases were always above 0.93, demonstrating the model\u2019s ability to detect inaccurate heart rate measurements.<\/jats:p>","DOI":"10.1007\/s10916-026-02412-2","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T06:31:09Z","timestamp":1779085869000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Motion-Based Confidence Score to Support the Practical Application of rPPG Methods in Health Monitoring"],"prefix":"10.1007","volume":"50","author":[{"given":"Miguel","family":"Arevalillo-Herr\u00e1ez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Tilbury","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"2412_CR1","doi-asserted-by":"crossref","unstructured":"Shin, W., Cha, Y. D., and Yoon, G., ECG\/PPG integer signal processing for a ubiquitous health monitoring system. J. Med. Syst. 34:891\u2013898, 2010.","DOI":"10.1007\/s10916-009-9304-7"},{"key":"2412_CR2","doi-asserted-by":"crossref","unstructured":"Lan, K. -c., Raknim, P., Kao, W. -F., and Huang, J. -H., Toward hypertension prediction based on PPG-derived HRV signals: A feasibility study. J. Med. Syst. 42:1\u20137, 2018.","DOI":"10.1007\/s10916-018-0942-5"},{"key":"2412_CR3","doi-asserted-by":"crossref","unstructured":"Saikevi\u010dius, L., Raudonis, V., Kozlovskaja-Gumbrien\u0117, A., and \u0160akalyt\u0117, G., Advancements in remote photoplethysmography. Electronics 14(5):1015, 2025.","DOI":"10.3390\/electronics14051015"},{"key":"2412_CR4","doi-asserted-by":"publisher","unstructured":"Gupta, A., Ravelo-Garc\u00eda, A. G., and Dias, F. M., Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review. Comput. Meth. Programs Biomed. 219:106771, 2022. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106771","DOI":"10.1016\/j.cmpb.2022.106771"},{"key":"2412_CR5","doi-asserted-by":"publisher","unstructured":"Qiao, D., Ayesha, A. H., Zulkernine, F., Jaffar, N., and Masroor, R., Revise: Remote vital signs measurement using smartphone camera. IEEE Access 10:131656\u2013131670, 2022. https:\/\/doi.org\/10.1109\/ACCESS.2022.3229977","DOI":"10.1109\/ACCESS.2022.3229977"},{"key":"2412_CR6","doi-asserted-by":"publisher","unstructured":"Hao, Z., Wang, J., Zhang, G., Gao, L., Zhang, X., Liu, J., Zhang, X., Yang, X., and Lai, Z., PPG heart rate extraction algorithm based on the motion artifact intensity classification and removal framework. Biomed. Signal Process. Contr. 94:106287, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2024.106287","DOI":"10.1016\/j.bspc.2024.106287"},{"key":"2412_CR7","doi-asserted-by":"publisher","unstructured":"Ouzar, Y., Djeldjli, D., Bousefsaf, F., and Maaoui, C., X-ippgnet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation. Comput. Biol. Med. 154:106592, 2023. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106592","DOI":"10.1016\/j.compbiomed.2023.106592"},{"key":"2412_CR8","doi-asserted-by":"crossref","unstructured":"Siddiqui, S. A., Zhang, Y., Feng, Z., and Kos, A., A pulse rate estimation algorithm using ppg and smartphone camera. J. Med. Syst. 40:1\u20136, 2016.","DOI":"10.1007\/s10916-016-0485-6"},{"key":"2412_CR9","doi-asserted-by":"publisher","unstructured":"Khodabakhshi, M. B., Eslamyeh, N., Sadredini, S. Z., and Ghamari, M., Cuffless blood pressure estimation using chaotic features of photoplethysmograms and parallel convolutional neural network. Comput. Meth. Programs Biomed. 226:107131, 2022. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107131","DOI":"10.1016\/j.cmpb.2022.107131"},{"key":"2412_CR10","doi-asserted-by":"publisher","unstructured":"Manullang, M. C. T., Lin, Y.-H., and Chou, N.-K., A transformer-based network for estimating blood pressure using facial videos. IEEE Sensors J. 25:1969\u20131977, 2025. https:\/\/doi.org\/10.1109\/jsen.2024.3496115","DOI":"10.1109\/jsen.2024.3496115"},{"key":"2412_CR11","doi-asserted-by":"crossref","unstructured":"Chowdhury, M. H., Reaz, M. B. I., Ali, S. H. M., Khan, M. S., and Chowdhury, M. E., ROSE-Net: Leveraging remote photoplethysmography to estimate oxygen saturation using deep learning. Biomed. Signal Process. Contr. 100:107105, 2025.","DOI":"10.1016\/j.bspc.2024.107105"},{"key":"2412_CR12","doi-asserted-by":"publisher","unstructured":"Bousefsaf, F., Djeldjli, D., Ouzar, Y., Maaoui, C., and Pruski, A., iPPG 2 cPPG: Reconstructing contact from imaging photoplethysmographic signals using u-net architectures. Comput. Biol. Med. 138:104860, 2021. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104860","DOI":"10.1016\/j.compbiomed.2021.104860"},{"key":"2412_CR13","doi-asserted-by":"publisher","unstructured":"Alnaggar, M., Siam, A. I., Handosa, M., Medhat, T., and Rashad, M. Z., Video-based real-time monitoring for heart rate and respiration rate. Expert Syst. Appl. 225:120135, 2023. https:\/\/doi.org\/10.1016\/j.eswa.2023.120135","DOI":"10.1016\/j.eswa.2023.120135"},{"key":"2412_CR14","doi-asserted-by":"crossref","unstructured":"Wallace, B., Kassab, L. Y., Law, A., Goubran, R., and Knoefel, F., Contactless remote assessment of heart rate and respiration rate using video magnification. IEEE Instrument. Measurem. Magaz. 25(1):20\u201327, 2022.","DOI":"10.1109\/MIM.2022.9693458"},{"key":"2412_CR15","doi-asserted-by":"crossref","unstructured":"Yu, Z., Li, X., and Zhao, G., Facial-video-based physiological signal measurement: Recent advances and affective applications. IEEE Signal Process. Magaz. 38(6):50\u201358, 2021.","DOI":"10.1109\/MSP.2021.3106285"},{"key":"2412_CR16","doi-asserted-by":"publisher","unstructured":"Guo, Y., Liu, X., Peng, S., Jiang, X., Xu, K., Chen, C., Wang, Z., Dai, C., and Chen, W., A review of wearable and unobtrusive sensing technologies for chronic disease management. Comput. Biol. Med. 129:104163, 2021. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104163","DOI":"10.1016\/j.compbiomed.2020.104163"},{"key":"2412_CR17","doi-asserted-by":"crossref","unstructured":"Selvaraju, V., Spicher, N., Wang, J., Ganapathy, N., Warnecke, J. M., Leonhardt, S., Swaminathan, R., and Deserno, T. M., Continuous monitoring of vital signs using cameras: A systematic review. Sensors 22(11):4097, 2022.","DOI":"10.3390\/s22114097"},{"key":"2412_CR18","doi-asserted-by":"publisher","unstructured":"Ryu, J., Hong, S., Liang, S., Pak, S., Chen, Q., and Yan, S., A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method. Comput. Meth. Programs Biomed. 200:105824, 2021. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105824","DOI":"10.1016\/j.cmpb.2020.105824"},{"key":"2412_CR19","doi-asserted-by":"crossref","unstructured":"Guler, S., Ozturk, O., Golparvar, A., Dogan, H., and Yapici, M. K., Effects of illuminance intensity on the green channel of remote photoplethysmography (rPPG) signals. Phys. Eng. Sci. Med. 1\u20137, 2022.","DOI":"10.1007\/s13246-022-01175-7"},{"key":"2412_CR20","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, H., and Lu, F., Assessment of deep learning-based heart rate estimation using remote photoplethysmography under different illuminations. IEEE Trans. Human-Mach. Syst. 52(6):1236\u20131246, 2022.","DOI":"10.1109\/THMS.2022.3207755"},{"key":"2412_CR21","doi-asserted-by":"crossref","unstructured":"Premkumar, S., and Hemanth, D. J., Intelligent remote photoplethysmography-based methods for heart rate estimation from face videos: A survey. In: Informatics, vol. 9, p. 57, 2022. MDPI","DOI":"10.3390\/informatics9030057"},{"key":"2412_CR22","doi-asserted-by":"crossref","unstructured":"Rouast, P. V., Adam, M. T., Chiong, R., Cornforth, D., and Lux, E., Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front. Comput. Sci. 12(5):858\u2013872, 2018.","DOI":"10.1007\/s11704-016-6243-6"},{"key":"2412_CR23","doi-asserted-by":"crossref","unstructured":"McDuff, D. J., Estepp, J. R., Piasecki, A. M., and Blackford, E. B., A survey of remote optical photoplethysmographic imaging methods. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6398\u20136404, 2015. IEEE","DOI":"10.1109\/EMBC.2015.7319857"},{"key":"2412_CR24","doi-asserted-by":"crossref","unstructured":"Gupta, A., Ravelo-Garc\u00eda, A. G., and Dias, F. M., Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review. Comput. Meth. Programs Biomed. 106771, 2022.","DOI":"10.1016\/j.cmpb.2022.106771"},{"key":"2412_CR25","doi-asserted-by":"publisher","unstructured":"Haque, M. A., Irani, R., Nasrollahi, K., and Moeslund, T. B., Heartbeat rate measurement from facial video. IEEE Intell. Syst. 31(3):40\u201348, 2016. https:\/\/doi.org\/10.1109\/MIS.2016.20","DOI":"10.1109\/MIS.2016.20"},{"key":"2412_CR26","doi-asserted-by":"crossref","unstructured":"Malasinghe, L., Katsigiannis, S., Ramzan, N., and Dahal, K., Remote heart rate extraction using microsoft kinecttm v2. 0. In: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology, pp. 1\u20136, 2018.","DOI":"10.1145\/3232059.3232060"},{"key":"2412_CR27","doi-asserted-by":"crossref","unstructured":"Smith, S. W., et al., Moving average filters. The scientist and engineer\u2019s guide to digital signal processing, 277\u2013284, 2003.","DOI":"10.1016\/B978-0-7506-7444-7\/50052-2"},{"key":"2412_CR28","doi-asserted-by":"publisher","unstructured":"Tanabe, J., Miller, D., Tregellas, J., Freedman, R., and Meyer, F. G., Comparison of detrending methods for optimal fmri preprocessing. NeuroImage 15(4):902\u2013907, 2002. https:\/\/doi.org\/10.1006\/nimg.2002.1053","DOI":"10.1006\/nimg.2002.1053"},{"key":"2412_CR29","doi-asserted-by":"publisher","unstructured":"Smilkstein, T., Buenrostro, M., Kenyon, A., Lienemann, M., and Larson, G., Heart rate monitoring using kinect and color amplification. In: 2014 IEEE Healthcare Innovation Conference (HIC), pp. 60\u201362, 2014. https:\/\/doi.org\/10.1109\/HIC.2014.7038874","DOI":"10.1109\/HIC.2014.7038874"},{"key":"2412_CR30","doi-asserted-by":"publisher","unstructured":"Cheng, J., Chen, X., Xu, L., Wang, Z. J., Illumination variation-resistant video-based heart rate measurement using joint blind source separation and ensemble empirical mode decomposition. IEEE J. Biomed. Health Inf. 21(5):1422\u20131433, 2017. https:\/\/doi.org\/10.1109\/JBHI.2016.2615472","DOI":"10.1109\/JBHI.2016.2615472"},{"key":"2412_CR31","doi-asserted-by":"publisher","unstructured":"Bakhtiyari, K., Beckmann, N., and Ziegler, J., Contactless heart rate variability measurement by ir and 3d depth sensors with respiratory sinus arrhythmia. Procedia Comput. Sci. 109:498\u2013505, 2017. https:\/\/doi.org\/10.1016\/j.procs.2017.05.319. 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, Portugal","DOI":"10.1016\/j.procs.2017.05.319"},{"key":"2412_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, C., Wu, X., Zhang, L., He, X., and Lv, Z., Simultaneous detection of blink and heart rate using multi-channel ica from smart phone videos. Biomed. Signal Process. Contr. 33:189\u2013200, 2017. https:\/\/doi.org\/10.1016\/j.bspc.2016.11.022","DOI":"10.1016\/j.bspc.2016.11.022"},{"key":"2412_CR33","doi-asserted-by":"crossref","unstructured":"Macwan, R., Benezeth, Y., and Mansouri, A., Remote photoplethysmography with constrained ica using periodicity and chrominance constraints. Biomed. Eng. Online 17(1):1\u201322, 2018.","DOI":"10.1186\/s12938-018-0450-3"},{"key":"2412_CR34","doi-asserted-by":"publisher","unstructured":"Wiede, C., Richter, J., and Hirtz, G., Signal fusion based on intensity and motion variations for remote heart rate determination. In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 526\u2013531, 2016. https:\/\/doi.org\/10.1109\/IST.2016.7738282","DOI":"10.1109\/IST.2016.7738282"},{"key":"2412_CR35","doi-asserted-by":"publisher","unstructured":"Bogdan, G., Radu, V., Octavian, F., Alin, B., Constantin, M., and Cristian, C., Remote assessment of heart rate by skin color processing. In 2015 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 112\u2013116, 2015. https:\/\/doi.org\/10.1109\/BlackSeaCom.2015.7185097","DOI":"10.1109\/BlackSeaCom.2015.7185097"},{"key":"2412_CR36","doi-asserted-by":"crossref","unstructured":"Janssen, R., Wang, W., Mo\u00e7o, A., and De\u00a0Haan, G., Video-based respiration monitoring with automatic region of interest detection. Physiol. Measurem. 37(1):100, 2015.","DOI":"10.1088\/0967-3334\/37\/1\/100"},{"key":"2412_CR37","doi-asserted-by":"publisher","unstructured":"Sharma, H., Heart rate extraction from PPG signals using variational mode decomposition. Biocybern. Biomed. Eng. 39(1):75\u201386, 2019. https:\/\/doi.org\/10.1016\/j.bbe.2018.11.001","DOI":"10.1016\/j.bbe.2018.11.001"},{"key":"2412_CR38","doi-asserted-by":"publisher","unstructured":"Bosi, I., Cogerino, C., and Bazzani, M., Real-time monitoring of heart rate by processing of microsoft kinect\u2122 2.0 generated streams. In: 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech), pp. 1\u20136, 2016. https:\/\/doi.org\/10.1109\/SpliTech.2016.7555944","DOI":"10.1109\/SpliTech.2016.7555944"},{"key":"2412_CR39","doi-asserted-by":"publisher","unstructured":"Wang, W., Brinker, A. C., Stuijk, S., and Haan, G., Algorithmic principles of remote PPG. IEEE Trans. Biomed. Eng. 64(7):1479\u20131491, 2017. https:\/\/doi.org\/10.1109\/TBME.2016.2609282","DOI":"10.1109\/TBME.2016.2609282"},{"key":"2412_CR40","doi-asserted-by":"crossref","unstructured":"De\u00a0Haan, G., and Jeanne, V., Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10):2878\u20132886, 2013.","DOI":"10.1109\/TBME.2013.2266196"},{"key":"2412_CR41","doi-asserted-by":"publisher","unstructured":"Chwyl, B., Chung, A. G., Amelard, R., Deglint, J., Clausi, D. A., and Wong, A., Sapphire: Stochastically acquired photoplethysmogram for heart rate inference in realistic environments. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1230\u20131234, 2016. https:\/\/doi.org\/10.1109\/ICIP.2016.7532554","DOI":"10.1109\/ICIP.2016.7532554"},{"key":"2412_CR42","doi-asserted-by":"publisher","unstructured":"Qi, H., Guo, Z., Chen, X., Shen, Z., and Jane Wang, Z., Video-based human heart rate measurement using joint blind source separation. Biomed. Signal Process. Control. 31:309\u2013320, 2017. https:\/\/doi.org\/10.1016\/j.bspc.2016.08.020","DOI":"10.1016\/j.bspc.2016.08.020"},{"key":"2412_CR43","doi-asserted-by":"publisher","unstructured":"Xing, W., Shi, Y., Wu, C., Wang, Y., and Wang, X., Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique. Comput. Biol. Med. 164:107112, 2023. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107112","DOI":"10.1016\/j.compbiomed.2023.107112"},{"key":"2412_CR44","doi-asserted-by":"publisher","unstructured":"Li, X., Chen, J., Zhao, G., and Pietik\u00e4inen, M., Remote heart rate measurement from face videos under realistic situations. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264\u20134271, 2014. https:\/\/doi.org\/10.1109\/CVPR.2014.543","DOI":"10.1109\/CVPR.2014.543"},{"key":"2412_CR45","doi-asserted-by":"publisher","unstructured":"Kado, S., Monno, Y., Moriwaki, K., Yoshizaki, K., Tanaka, M., and Okutomi, M., Remote heart rate measurement from rgb-nir video based on spatial and spectral face patch selection. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5676\u20135680, 2018. https:\/\/doi.org\/10.1109\/EMBC.2018.8513464","DOI":"10.1109\/EMBC.2018.8513464"},{"key":"2412_CR46","doi-asserted-by":"publisher","unstructured":"Poh, M. -Z., McDuff, D. J., and Picard, R. W., Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express. 18(10):10762\u201310774, 2010. https:\/\/doi.org\/10.1364\/OE.18.010762","DOI":"10.1364\/OE.18.010762"},{"key":"2412_CR47","doi-asserted-by":"crossref","unstructured":"Allado, E., Poussel, M., Renno, J., Moussu, A., Hily, O., Temperelli, M., Albuisson, E., and Chenuel, B., Remote photoplethysmography is an accurate method to remotely measure respiratory rate: A hospital-based trial. J. Clin. Med. 11(13):3647, 2022.","DOI":"10.3390\/jcm11133647"},{"key":"2412_CR48","doi-asserted-by":"crossref","unstructured":"Finotti, G., Di\u00a0Lernia, D., Tsakiris, M., Riva, G., and Naber, M., Remote photoplethysmography (rPPG) in the wild: Remote heart rate imaging via online webcams, 2022.","DOI":"10.31234\/osf.io\/v89zn"},{"key":"2412_CR49","doi-asserted-by":"publisher","unstructured":"Ghorbani, R., Reinders, M. J. T., and Tax, D. M. J., Personalized anomaly detection in ppg data using representation learning and biometric identification. Biomed. Signal Process. Control. 94:106216, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2024.106216","DOI":"10.1016\/j.bspc.2024.106216"},{"key":"2412_CR50","doi-asserted-by":"publisher","unstructured":"Jaimme\u00a0Poppen, C. D., Kumar, N. J., Karthik, S., Margana, B. S., Sivaprakasam, M., and Joseph, J., Fusion of ballistocardiography and imaging for improved non-contact heart rate monitoring. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/EMBC53108.2024.10781858","DOI":"10.1109\/EMBC53108.2024.10781858"},{"key":"2412_CR51","doi-asserted-by":"crossref","unstructured":"Ernst, H., Malberg, H., and Schmidt, M., More reliable remote heart rate measurement by signal quality indexes. In: 2020 Computing in Cardiology, pp. 1\u20134, 2020. IEEE","DOI":"10.22489\/CinC.2020.165"},{"key":"2412_CR52","doi-asserted-by":"crossref","unstructured":"Fan, X., and Tjahjadi, T., Robust contactless pulse transit time estimation based on signal quality metric. Patt. Recogn. Lett. 137:12\u201316, 2020.","DOI":"10.1016\/j.patrec.2019.06.016"},{"key":"2412_CR53","doi-asserted-by":"publisher","unstructured":"Kraft, D., Bieber, G., and Fellmann, M., Reliability factor for accurate remote PPG systems. In: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments. PETRA \u201923, pp. 448\u2013456. Association for Computing Machinery, New York, NY, USA, 2023. https:\/\/doi.org\/10.1145\/3594806.3596573 .","DOI":"10.1145\/3594806.3596573"},{"key":"2412_CR54","doi-asserted-by":"publisher","unstructured":"Gao, H., Wu, X., Shi, C., Gao, Q., and Geng, J., A LSTM-based realtime signal quality assessment for photoplethysmogram and remote photoplethysmogram. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3826\u20133835, 2021. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00424","DOI":"10.1109\/CVPRW53098.2021.00424"},{"key":"2412_CR55","doi-asserted-by":"publisher","unstructured":"Liu, X., Yang, X., and Li, X., Hrunet: Assessing uncertainty in heart rates measured from facial videos. IEEE J. Biomed. Health Inf. 28(5):2955\u20132966, 2024. https:\/\/doi.org\/10.1109\/JBHI.2024.3363006","DOI":"10.1109\/JBHI.2024.3363006"},{"key":"2412_CR56","doi-asserted-by":"publisher","unstructured":"Song, R., Wang, H., Xia, H., Cheng, J., Li, C., and Chen, X., Uncertainty quantification for deep learning-based remote photoplethysmography. IEEE Trans. Instrument. Measurem. 72:1\u201312, 2023. https:\/\/doi.org\/10.1109\/TIM.2023.3317379","DOI":"10.1109\/TIM.2023.3317379"},{"key":"2412_CR57","doi-asserted-by":"crossref","unstructured":"Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al., A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(Suppl 1):1513\u20131589, 2023.","DOI":"10.1007\/s10462-023-10562-9"},{"key":"2412_CR58","unstructured":"Kendall, A., and Gal, Y., What uncertainties do we need in bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30, 2017."},{"key":"2412_CR59","unstructured":"ITU-T RECOMMENDATION P. 910. Subjective video quality assessment methods for multimedia applications, 2022."},{"key":"2412_CR60","doi-asserted-by":"publisher","unstructured":"Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., and Dubois, J., Unsupervised skin tissue segmentation for remote photoplethysmography. Patt. Recogn. Lett. 124:82\u201390, 2019. https:\/\/doi.org\/10.1016\/j.patrec.2017.10.017 . Award Winning Papers from the 23rd International Conference on Pattern Recognition (ICPR)","DOI":"10.1016\/j.patrec.2017.10.017"},{"key":"2412_CR61","doi-asserted-by":"crossref","unstructured":"Pilz, C. S., Zaunseder, S., Krajewski, J., and Blazek, V., Local group invariance for heart rate estimation from face videos in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1254\u20131262, 2018.","DOI":"10.1109\/CVPRW.2018.00172"},{"key":"2412_CR62","doi-asserted-by":"crossref","unstructured":"Stricker, R., M\u00fcller, S., and Gross, H.-M., Non-contact video-based pulse rate measurement on a mobile service robot. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 1056\u20131062, 2014. IEEE","DOI":"10.1109\/ROMAN.2014.6926392"},{"key":"2412_CR63","unstructured":"Heusch, G., Anjos, A., and Marcel, S., A reproducible study on remote heart rate measurement, 2017. arXiv preprint arXiv:1709.00962"},{"key":"2412_CR64","doi-asserted-by":"crossref","unstructured":"Revanur, A., Li, Z., Ciftci, U. A., Yin, L., and Jeni, L. A., The first vision for vitals (v4v) challenge for non-contact video-based physiological estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2760\u20132767.","DOI":"10.1109\/ICCVW54120.2021.00310"},{"key":"2412_CR65","doi-asserted-by":"publisher","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., and Pantic, M., A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1):42\u201355, 2012. https:\/\/doi.org\/10.1109\/T-AFFC.2011.25","DOI":"10.1109\/T-AFFC.2011.25"},{"key":"2412_CR66","doi-asserted-by":"crossref","unstructured":"Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., and Dubois, J., Unsupervised skin tissue segmentation for remote photoplethysmography. Patt. Recogn. Lett. 124:82\u201390, 2019.","DOI":"10.1016\/j.patrec.2017.10.017"},{"key":"2412_CR67","unstructured":"Pilz, C. S., Zaunseder, S., Krajewski, J., and Blazek, V., Local group invariance for heart rate estimation from face videos in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1254\u20131262."},{"key":"2412_CR68","doi-asserted-by":"crossref","unstructured":"Stricker, R., M\u00fcller, S., and Gross, H. -M., Non-contact video-based pulse rate measurement on a mobile service robot. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 1056\u20131062. IEEE","DOI":"10.1109\/ROMAN.2014.6926392"},{"key":"2412_CR69","unstructured":"Bazarevsky, V., Kartynnik, Y., Vakunov, A., Raveendran, K., and Grundmann, M., Blazeface: Sub-millisecond neural face detection on mobile gpus, 2019. arXiv preprint arXiv:1907.05047"},{"key":"2412_CR70","doi-asserted-by":"publisher","unstructured":"Reiser, M., Amft, O., and Breidenassel, A., Analysis of melanin concentration on reflective pulse oximetry using monte carlo simulations. IEEE Access 13:24454\u201324462, 2025. https:\/\/doi.org\/10.1109\/ACCESS.2025.3538281","DOI":"10.1109\/ACCESS.2025.3538281"},{"key":"2412_CR71","doi-asserted-by":"crossref","unstructured":"Casado, C. A., and L\u00f3pez, M. B., Face2ppg: An unsupervised pipeline for blood volume pulse extraction from faces. IEEE J. Biomed. Health Inf., 2023.","DOI":"10.1109\/JBHI.2023.3307942"},{"key":"2412_CR72","doi-asserted-by":"crossref","unstructured":"Sun, Z., and Li, X., Contrast-phys: Unsupervised video-based remote physiological measurement via spatiotemporal contrast. In: European Conference on Computer Vision, pp. 492\u2013510, 2022. Springer","DOI":"10.1007\/978-3-031-19775-8_29"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02412-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-026-02412-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02412-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T06:31:32Z","timestamp":1779085892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-026-02412-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":72,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2412"],"URL":"https:\/\/doi.org\/10.1007\/s10916-026-02412-2","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]},"assertion":[{"value":"9 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Ethics and Consent to Participate declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"82"}}