{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T23:00:32Z","timestamp":1784847632075,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformit\u00e9 Europ\u00e9enne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates\u2019 determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman\u2019s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman\u2019s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.<\/jats:p>","DOI":"10.3390\/s24123766","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:59:06Z","timestamp":1718009946000},"page":"3766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6689-2847","authenticated-orcid":false,"given":"Guylian","family":"Stevens","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Systems\u2014IBiTech, Korneel Heymanslaan, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc","family":"Hantson","sequence":"additional","affiliation":[{"name":"H3CareSolutions, Henegouwestraat 41, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michiel","family":"Larmuseau","sequence":"additional","affiliation":[{"name":"AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan R.","family":"Heerman","sequence":"additional","affiliation":[{"name":"Partnership of Anesthesia of the AZ Maria Middelares Hospital, Buitenring Sint-Denijs 30, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Siau","sequence":"additional","affiliation":[{"name":"Alsico NV, Pont West, 9600 Ronse, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Verdonck","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Systems\u2014IBiTech, Korneel Heymanslaan, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022). Ageing and Health, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1082183","DOI":"10.3389\/fpubh.2022.1082183","article-title":"Projecting the chronic disease burden among the adult population in the United States using a multi-state population model","volume":"10","author":"Ansah","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S1057-6290(08)10002-X","article-title":"No longer a patient: The social construction of the medical consumer","volume":"Volume 10","author":"Goldner","year":"2008","journal-title":"Advances in Medical Sociology"},{"key":"ref_4","unstructured":"Saltman, R.B., Busse, R., and Figueras, J. (2004). Social Health Insurance Systems in Western Europe, Open University Press."},{"key":"ref_5","unstructured":"Balas, V.E., Solanki, V.K., and Kumar, R. (2020). Chapter 6\u2014The growing role of internet of things in healthcare wearables. Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, Academic Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hickey, A. (2021). The rise of wearables: From innovation to implementation. Digital Health, Elsevier.","DOI":"10.1016\/B978-0-12-818914-6.00012-0"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41746-021-00418-3","article-title":"The emerging clinical role of wearables: Factors for successful implementation in healthcare","volume":"4","author":"Smuck","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103314","DOI":"10.1016\/j.drudis.2022.06.014","article-title":"Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: Transforming the healthcare applications","volume":"27","author":"Beg","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Almotiri, S.H., Khan, M.A., and Alghamdi, M.A. (2016, January 22\u201324). Mobile health (m-health) system in the context of IoT. Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Vienna, Austria.","DOI":"10.1109\/W-FiCloud.2016.24"},{"key":"ref_10","first-page":"133","article-title":"The LifeShirt: A multi-function ambulatory system monitoring health, disease, and medical intervention in the real world","volume":"108","author":"Grossman","year":"2004","journal-title":"Stud. Health Technol. Inf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4","DOI":"10.4258\/hir.2017.23.1.4","article-title":"Wearable devices in medical internet of things: Scientific research and commercially available devices","volume":"23","author":"Haghi","year":"2017","journal-title":"Healthc. Inform. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.amjcard.2022.06.020","article-title":"At the Crossroads! Time to Start Taking Smartwatches Seriously","volume":"179","author":"Lima","year":"2022","journal-title":"Am. J. Cardiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sinhal, R., Singh, K., and Raghuwanshi, M. (2020, January 26\u201327). An overview of remote photoplethysmography methods for vital sign monitoring. Proceedings of the Computer Vision and Machine Intelligence in Medical Image Analysis: International Symposium, ISCMM 2019, Online.","DOI":"10.1007\/978-981-13-8798-2_3"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100301","DOI":"10.1088\/1361-6579\/ac2d82","article-title":"Photoplethysmography (PPG): State-of-the-art methods and applications","volume":"42","author":"Allen","year":"2021","journal-title":"Physiol. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_16","first-page":"246","article-title":"Application of Organic Photodetectors (OPD) in Photoplethysmography (PPG) Sensors: A small review","volume":"8","author":"Jhuma","year":"2022","journal-title":"Proc. Int. Exch. Innov. Conf. Eng. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Galli, A., Frigo, G., Narduzzi, C., and Giorgi, G. (2017, January 22\u201325). Robust estimation and tracking of heart rate by PPG signal analysis. Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Torino, Italy.","DOI":"10.1109\/I2MTC.2017.7969715"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1088\/0967-3334\/37\/4\/610","article-title":"An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram","volume":"37","author":"Charlton","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.trf.2019.09.015","article-title":"HeartPy: A novel heart rate algorithm for the analysis of noisy signals","volume":"66","author":"Farah","year":"2019","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4103\/ijoy.IJOY_27_17","article-title":"Breath Rate Variability: A Novel Measure to Study the Meditation Effects","volume":"12","author":"Soni","year":"2019","journal-title":"Int. J. Yoga"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sarkar, S., Bhattacherjee, S., and Pal, S. (2015). Extraction of Respiration Signal from ECG for Respiratory Rate Estimation, IET.","DOI":"10.1049\/cp.2015.1654"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mohan, P.M., Nisha, A.A., Nagarajan, V., and Jothi, E.S.J. (2016, January 6\u20138). Measurement of arterial oxygen saturation (SpO2) using PPG optical sensor. Proceedings of the 2016 International Conference on Communication and Signal Processing (ICCSP), Tamilnadu, India.","DOI":"10.1109\/ICCSP.2016.7754330"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Haque, C.A., Hossain, S., Kwon, T.H., and Kim, K.D. (2021, January 20\u201322). Comparison of different methods to estimate blood oxygen saturation using PPG. Proceedings of the 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC52510.2021.9621142"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, G., Zhu, W., Zhong, J., Tong, T., Zhang, J., and Wang, L. (2018, January 8\u201312). Wearable ear blood oxygen saturation and pulse measurement system based on PPG. Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Guangzhou, China.","DOI":"10.1109\/SmartWorld.2018.00054"},{"key":"ref_25","unstructured":"Fung, P., Dumont, G., Ries, C., Mott, C., and Ansermino, M. (2004, January 1\u20135). Continuous noninvasive blood pressure measurement by pulse transit time. Proceedings of the the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ballaji, H.K., Correia, R., Korposh, S., Hayes-Gill, B.R., Hernandez, F.U., Salisbury, B., and Morgan, S.P. (2020). A Textile Sleeve for Monitoring Oxygen Saturation Using Multichannel Optical Fibre Photoplethysmography. Sensors, 20.","DOI":"10.3390\/s20226568"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MC.2004.1297238","article-title":"Healthwear: Medical technology becomes wearable","volume":"37","author":"Pentland","year":"2004","journal-title":"Computer"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10916-010-9505-0","article-title":"Relationship between measurement site and motion artifacts in wearable reflected photoplethysmography","volume":"35","author":"Maeda","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, S., Shin, H., and Hahm, C. (February, January 31). Effective PPG sensor placement for reflected red and green light, and infrared wristband-type photoplethysmography. Proceedings of the 2016 18th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, Republic of Korea.","DOI":"10.1109\/ICACT.2016.7423469"},{"key":"ref_30","unstructured":"(2024, March 12). scipy.signal.find_peaks\u2014SciPy v1.13.1 Manual. Available online: https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.signal.find_peaks.html."},{"key":"ref_31","unstructured":"Van Gent, P., Farah, H., and Gent, V. (2018, January 13\u201314). Heart Rate Analysis for Human Factors: Development and Validation of an Open Source Toolkit for Noisy Naturalistic Heart Rate Data. Proceedings of the 6th HUMMANIST Conference, The Hague, The Netherlands. Available online: https:\/\/www.researchgate.net\/publication\/325967542_Heart_Rate_Analysis_for_Human_Factors_Development_and_Validation_of_an_Open_Source_Toolkit_for_Noisy_Naturalistic_Heart_Rate_Data."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e76585","DOI":"10.1371\/journal.pone.0076585","article-title":"Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions","volume":"8","author":"Elgendi","year":"2013","journal-title":"PLoS ONE"},{"key":"ref_34","unstructured":"Vallat, R. (2024, March 12). Available online: https:\/\/gist.github.com\/raphaelvallat\/55624e2eb93064ae57098dd96f259611#file-ecg_derived_respiration-ipynb."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20013","DOI":"10.1063\/1.5142105","article-title":"Development of wearable optical-based fibre sensor system for pulse transit time measurement","volume":"2203","author":"Zaki","year":"2020","journal-title":"AIP Conf. Proc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Iakovlev, D., Hu, S., Hassan, H., Dwyer, V., Ashayer-Soltani, R., Hunt, C., and Shen, J. (2018). Smart Garment Fabrics to Enable Non-Contact Opto-Physiological Monitoring. Biosensors, 8.","DOI":"10.3390\/bios8020033"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TBCAS.2007.910900","article-title":"Multichannel reflective PPG earpiece sensor with passive motion cancellation","volume":"1","author":"Wang","year":"2007","journal-title":"IEEE Trans. Biomed. Circ. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"25681","DOI":"10.3390\/s151025681","article-title":"A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise","volume":"15","author":"Alzahrani","year":"2015","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1109\/JSEN.2012.2235424","article-title":"Development and evaluation of a wristwatch-type photoplethysmography array sensor module","volume":"13","author":"Lee","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.det.2009.08.006","article-title":"Skin type classification systems old and new","volume":"27","author":"Roberts","year":"2009","journal-title":"Dermatol. Clin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1152\/advan.00068.2004","article-title":"Archive Additions","volume":"29","author":"Prakash","year":"2005","journal-title":"Adv. Physiol. Educ."},{"key":"ref_42","first-page":"10","article-title":"Understanding Bland Altman Analysis","volume":"19","author":"Vesna","year":"2009","journal-title":"Biochem. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1177\/172460080201700213","article-title":"Bravais-Pearson and Spearman correlation coefficients: Meaning, test of hypothesis and confidence interval","volume":"17","author":"Artusi","year":"2002","journal-title":"Int. J. Biol. Mark."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1088\/0967-3334\/25\/5\/003","article-title":"Agreement between three commercially available instruments for measuring short-term heart rate variability","volume":"25","author":"Sandercock","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shcherbina, A., Mattsson, C.M., Waggott, D., Salisbury, H., Christle, J.W., Hastie, T., Wheeler, M.T., and Ashley, E.A. (2017). Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort. J. Pers. Med., 7.","DOI":"10.3390\/jpm7020003"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3480","DOI":"10.1038\/s41598-023-30171-4","article-title":"Clinical validation of a contactless respiration rate monitor","volume":"13","author":"Bujan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e10828","DOI":"10.2196\/10828","article-title":"Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study","volume":"7","author":"Nelson","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/s41746-021-00493-6","article-title":"Measurement of respiratory rate using wearable devices and applications to COVID-19 detection","volume":"4","author":"Natarajan","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_49","unstructured":"Hinkle, D.E., Wiersma, W., and Jurs, S.G. (2003). Applied Statistics for the Behavioral Sciences, Houghton Mifflin Boston."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Asada, H.H., and Reisner, A. (2006, January 4\u201310). Wearable sensors for human health monitoring. Proceedings of the Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems\u2014SPIE, Long Beach, CA, USA.","DOI":"10.1117\/12.667764"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Orini, M., Guvensen, G., Jamieson, A., Chaturvedi, N., and Hughes, A.D. (2022, January 4\u20137). Movement, sweating, and contact pressure as sources of heart rate inaccuracy in wearable devices. Proceedings of the 2022 Computing in Cardiology (CinC), Tampere, Finland.","DOI":"10.22489\/CinC.2022.237"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fine, J., Branan, K.L., Rodriguez, A.J., Boonya-Ananta, T., Ramella-Roman, J.C., McShane, M.J., and Cot\u00e9, G.L. (2021). Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring. Biosensors, 11.","DOI":"10.3390\/bios11040126"},{"key":"ref_53","unstructured":"COPTECH WIRE & CABLE PVT Ltd. (2023). The Mysteries: How Wire and Cable Length Shape Signal Integrity and Performancele, COPTECH WIRE & CABLE PVT Ltd."},{"key":"ref_54","unstructured":"Song, K., Gao, J., Wang, Z., Ali, E., Bilal, M., and Xie, G. (2019, January 4\u20136). A method of improving signal integrity of solder joints. Proceedings of the 7th International Conference on Reliability of Electrical Products and Electrical Contacts, Suzhou, China."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Esgalhado, F., Fernandes, B., Vassilenko, V., Batista, A., and Russo, S. (2021). The application of deep learning algorithms for ppg signal processing and classification. Computers, 10.","DOI":"10.3390\/computers10120158"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10000","DOI":"10.1109\/JSEN.2020.2990864","article-title":"PP-Net: A deep learning framework for PPG-based blood pressure and heart rate estimation","volume":"20","author":"Panwar","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1007\/s11036-019-01323-6","article-title":"Detection and removal of motion artifacts in PPG signals","volume":"27","author":"Pollreisz","year":"2022","journal-title":"Mob. Netw. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Goh, C.H., Tan, L.K., Lovell, N.H., Ng, S.C., Tan, M.P., and Lim, E. (2020). Robust PPG motion artifact detection using a 1-D convolution neural network. Comput. Methods Progr. Biomed., 196.","DOI":"10.1016\/j.cmpb.2020.105596"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/TIM.2011.2175832","article-title":"A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter","volume":"61","author":"Ram","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_60","unstructured":"Shao, H., and Chen, X. (2017, January 28\u201330). Motion artifact detection and reduction in PPG signals based on statistics analysis. Proceedings of the 2017 29th Chinese Control and Decision Conference (CCDC), Chongqing, China."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.resuscitation.2016.02.005","article-title":"The value of vital sign trends for detecting clinical deterioration on the wards","volume":"102","author":"Churpek","year":"2016","journal-title":"Resuscitation"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"657","DOI":"10.5694\/j.1326-5377.2008.tb01825.x","article-title":"Respiratory rate: The neglected vital sign","volume":"188","author":"Cretikos","year":"2008","journal-title":"Med. J. Aust."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.resuscitation.2004.03.005","article-title":"Association between clinically abnormal observations and subsequent in-hospital mortality: A prospective study","volume":"62","author":"Buist","year":"2004","journal-title":"Resuscitation"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"489","DOI":"10.12968\/bjon.2006.15.9.21087","article-title":"Why don\u2019t nurses monitor the respiratory rates of patients?","volume":"15","author":"Hogan","year":"2006","journal-title":"Br. J. Nurs."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1378\/chest.12-1837","article-title":"Flash mob research: A single-day, multicenter, resident-directed study of respiratory rate","volume":"143","author":"Semler","year":"2013","journal-title":"Chest"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1111\/j.1478-5153.2011.00427.x","article-title":"Nursing documentation prior to emergency admissions to the intensive care unit","volume":"16","author":"Jonsson","year":"2011","journal-title":"Nurs. Crit. Care"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.annemergmed.2004.06.016","article-title":"The vexatious vital: Neither clinical measurements by nurses nor an electronic monitor provides accurate measurements of respiratory rate in triage","volume":"45","author":"Lovett","year":"2005","journal-title":"Ann. Emerg. Med."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Leenen, J.P.L., Dijkman, E.M., van Hout, A., Kalkman, C.J., Schoonhoven, L., and Patijn, G.A. (2022). Nurses\u2019 experiences with continuous vital sign monitoring on the general surgical ward: A qualitative study based on the Behaviour Change Wheel. BMC Nurs., 21.","DOI":"10.1186\/s12912-022-00837-x"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:56:15Z","timestamp":1760108175000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,10]]},"references-count":68,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123766"],"URL":"https:\/\/doi.org\/10.3390\/s24123766","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,10]]}}}