{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:16:53Z","timestamp":1775067413170,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Defense (DoD) of United States through Office of the Congressionally Directed Medical Research Programs (CDMRP)","award":["PR-182496"],"award-info":[{"award-number":["PR-182496"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work presents a study on users\u2019 attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user\u2019s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively<\/jats:p>","DOI":"10.3390\/s22208047","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8047","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2750-0147","authenticated-orcid":false,"given":"Pragya","family":"Sharma","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas B.","family":"Conroy","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3378-5225","authenticated-orcid":false,"given":"Xiaonan","family":"Hui","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edwin C.","family":"Kan","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2470","DOI":"10.1109\/JPROC.2013.2262913","article-title":"A Survey on Ambient Intelligence in Healthcare","volume":"101","author":"Acampora","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nakashima, H., Aghajan, H., and Augusto, J.C. (2010). Ambient Intelligence and Smart Environments: A State of the Art. Handbook of Ambient Intelligence and Smart Environments, Springer.","DOI":"10.1007\/978-0-387-93808-0"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1177\/1555343412445054","article-title":"Is More Information Better? How Dismounted Soldiers Use Video Feed from Unmanned Vehicles: Attention Allocation and Information Extraction Considerations","volume":"7","author":"Borowsky","year":"2013","journal-title":"J. Cogn. Eng. Decis. Mak."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jha, A.P., Morrison, A.B., Dainer-Best, J., Parker, S., Rostrup, N., and Stanley, E.A. (2015). Minds \u201cAt Attention\u201d: Mindfulness Training Curbs Attentional Lapses in Military Cohorts. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0116889"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1348\/000712601162103","article-title":"A Selective Review of Selective Attention Research from the Past Century","volume":"92","author":"Driver","year":"2001","journal-title":"Br. J. Psychol."},{"key":"ref_6","unstructured":"Sohlberg, M.M., and Mateer, C.A. (1989). Introduction to Cognitive Rehabilitation: Theory and Practice, Guilford Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.3758\/APP.71.5.1042","article-title":"Interactions between Endogenous and Exogenous Attention during Vigilance","volume":"71","author":"Maclean","year":"2009","journal-title":"Atten. Percept. Psychophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/TAFFC.2016.2515084","article-title":"Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate","volume":"8","author":"Monkaresi","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/TAFFC.2014.2316163","article-title":"The Faces of Engagement: Automatic Recognition of Student Engagement from Facial Expressions","volume":"5","author":"Whitehill","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"388","DOI":"10.3389\/fnhum.2017.00388","article-title":"Sustained Attention in Real Classroom Settings: An EEG Study","volume":"11","author":"Ko","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.cogbrainres.2005.04.011","article-title":"Effects of Mental Fatigue on Attention: An ERP Study","volume":"25","author":"Boksem","year":"2005","journal-title":"Cogn. Brain Res."},{"key":"ref_12","unstructured":"Tefft, B.C. (2014). AAA Foundation for Traffic Safety, AAA Foundation for Traffic Safety."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0301-0511(00)00085-5","article-title":"A Critical Review of the Psychophysiology of Driver Fatigue","volume":"55","author":"Lal","year":"2001","journal-title":"Biol. Psychol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.apergo.2013.04.020","article-title":"Detection of Vigilance Performance Using Eye Blinks","volume":"45","author":"McIntire","year":"2014","journal-title":"Appl. Ergon."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16937","DOI":"10.3390\/s121216937","article-title":"Detecting Driver Drowsiness Based on Sensors: A Review","volume":"12","author":"Sahayadhas","year":"2012","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/TITS.2019.2918328","article-title":"Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention","volume":"21","author":"Kashevnik","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","first-page":"78","article-title":"Real-Time System for Monitoring Driver Vigilance","volume":"7","author":"Bergasa","year":"2004","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10273","DOI":"10.3390\/s130810273","article-title":"Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors","volume":"13","author":"Liu","year":"2013","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Guzman, A. (2021). Effects of Mindfulness Meditation on Selective, Sustained Attention, Brain Neural Oscillations, and Short-Term Memory. [Bachelor\u2019s Thesis, University of Nebraska-Lincoln]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, G., and Chung, W.-Y. (2022). Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review. Sensors, 22.","DOI":"10.3390\/s22031100"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"173","DOI":"10.2114\/jpa2.27.173","article-title":"Use of Frequency Domain Analysis of Skin Conductance for Evaluation of Mental Workload","volume":"27","author":"Shimomura","year":"2008","journal-title":"J. Physiol. Anthropol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, C.-Y., Wang, C.-J., Chen, E.-L., Wu, C.-K., Yang, Y.K., Wang, J.-S., and Chung, P.-C. (2010, January 15\u201317). Detecting Sustained Attention during Cognitive Work Using Heart Rate Variability. Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Darmstadt, Germany.","DOI":"10.1109\/IIHMSP.2010.187"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6913","DOI":"10.3390\/s90906913","article-title":"Changes in Physiological Parameters Induced by Indoor Simulated Driving: Effect of Lower Body Exercise at Mid-Term Break","volume":"9","author":"Liang","year":"2009","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dzedzickis, A., Kaklauskas, A., and Bucinskas, V. (2020). Human Emotion Recognition: Review of Sensors and Methods. Sensors, 20.","DOI":"10.3390\/s20030592"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e13091","DOI":"10.1111\/psyp.13091","article-title":"Coupling of Respiration and Attention via the Locus Coeruleus: Effects of Meditation and Pranayama","volume":"55","author":"Melnychuk","year":"2018","journal-title":"Psychophysiology"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1111\/j.1469-8986.2006.00467.x","article-title":"Respiratory Sinus Arrhythmia, Emotion, and Emotion Regulation during Social Interaction","volume":"43","author":"Butler","year":"2006","journal-title":"Psychophysiology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1016\/S0735-1097(00)00595-7","article-title":"Effects of Controlled Breathing, Mental Activity and Mental Stress with or without Verbalization on Heart Rate Variability","volume":"35","author":"Luciano","year":"2000","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"874","DOI":"10.3389\/fpsyg.2017.00874","article-title":"The Effect of Diaphragmatic Breathing on Attention, Negative Affect and Stress in Healthy Adults","volume":"8","author":"Ma","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TSMCA.2005.855922","article-title":"A Probabilistic Framework for Modeling and Real-Time Monitoring Human Fatigue","volume":"36","author":"Ji","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41928-017-0001-0","article-title":"Monitoring Vital Signs over Multiplexed Radio by Near-Field Coherent Sensing","volume":"1","author":"Hui","year":"2018","journal-title":"Nat. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41746-020-0307-6","article-title":"Wearable Radio-Frequency Sensing of Respiratory Rate, Respiratory Volume, and Heart Rate","volume":"3","author":"Sharma","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TMTT.2013.2256924","article-title":"A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring","volume":"61","author":"Li","year":"2013","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5093","DOI":"10.1038\/srep05093","article-title":"Surface Chest Motion Decomposition for Cardiovascular Monitoring","volume":"4","author":"Shafiq","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.bspc.2014.03.004","article-title":"Non-Contact Heart Rate and Heart Rate Variability Measurements: A Review","volume":"13","author":"Kranjec","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gouveia, C., Vieira, J., and Pinho, P. (2019). A Review on Methods for Random Motion Detection and Compensation in Bio-Radar Systems. Sensors, 19.","DOI":"10.3390\/s19030604"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1080\/00223980009600858","article-title":"The Mackworth Clock Test: A Computerized Version","volume":"134","author":"Lichstein","year":"2000","journal-title":"J. Psychol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/JERM.2020.2998761","article-title":"A Wireless Wearable RF Sensor for Brumation Study of Chelonians","volume":"5","author":"Zhou","year":"2021","journal-title":"IEEE J. Electromagn. RF Microw. Med. Biol."},{"key":"ref_39","unstructured":"(2022, September 07). Ettus Research USRP B200mini. Available online: https:\/\/www.ettus.com\/all-products\/usrp-b200mini\/."},{"key":"ref_40","unstructured":"(2022, September 07). Data Acquisition and Analysis System with AcqKnowledge for MP36R. Available online: https:\/\/www.biopac.com\/product\/mp36r-systems\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1177\/0098628316677643","article-title":"PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires and Reaction-Time Experiments","volume":"44","author":"Stoet","year":"2017","journal-title":"Teach. Psychol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sharma, P., and Kan, E.C. (2018, January 10\u201315). Sleep Scoring with a UHF RFID Tag by Near Field Coherent Sensing. Proceedings of the 2018 IEEE\/MTT-S International Microwave Symposium-IMS, Philadelphia, PA, USA.","DOI":"10.1109\/MWSYM.2018.8439216"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"235","DOI":"10.30773\/pi.2017.08.17","article-title":"Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature","volume":"15","author":"Kim","year":"2018","journal-title":"Psychiatry Investig."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1109\/TPAMI.2008.26","article-title":"Emotion Recognition Based on Physiological Changes in Music Listening","volume":"30","author":"Kim","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1049\/htl.2017.0090","article-title":"Are Ultra-Short Heart Rate Variability Features Good Surrogates of Short-Term Ones? State-of-the-Art Review and Recommendations","volume":"5","author":"Pecchia","year":"2018","journal-title":"Healthc. Technol. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Milagro, J., Gil, E., Garz\u00f3n-Rey, J.M., Aguil\u00f3, J., and Bail\u00f3n, R. (2017, January 24\u201327). Inspiration and Expiration Dynamics in Acute Emotional Stress Assessment. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.261-411"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s10439-012-0668-3","article-title":"The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets","volume":"41","author":"Yentes","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/34.954607","article-title":"Toward Machine Emotional Intelligence: Analysis of Affective Physiological State","volume":"23","author":"Picard","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"528781","DOI":"10.1155\/2012\/528781","article-title":"An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram","volume":"2012","author":"Belle","year":"2012","journal-title":"Comput. Math. Methods Med."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Stancin, I., Frid, N., Cifrek, M., and Jovic, A. (2021). EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization. Sensors, 21.","DOI":"10.3390\/s21206932"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.eswa.2018.07.054","article-title":"Automatic Driver Sleepiness Detection Using EEG, EOG and Contextual Information","volume":"115","author":"Barua","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7235","DOI":"10.1016\/j.eswa.2010.12.028","article-title":"Applying Neural Network Analysis on Heart Rate Variability Data to Assess Driver Fatigue","volume":"38","author":"Patel","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/cplx.21391","article-title":"Evaluating Nonlinear Variability of Mental Fatigue Behavioral Indices during Long-Term Attentive Task","volume":"17","author":"Azarnoosh","year":"2012","journal-title":"Complexity"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5303","DOI":"10.1109\/JSEN.2020.3028970","article-title":"Furniture-Integrated Respiration Sensors by Notched Transmission Lines","volume":"21","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1046\/j.1440-1681.2000.03306.x","article-title":"Relative Timing of Inspiration and Expiration Affects Respiratory Sinus Arrhythmia","volume":"27","author":"Moser","year":"2000","journal-title":"Clin. Exp. Pharmacol. Physiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1080\/02699930143000392","article-title":"Respiratory Feedback in the Generation of Emotion","volume":"16","author":"Philippot","year":"2002","journal-title":"Cogn. Emot."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"397","DOI":"10.3389\/fnhum.2018.00397","article-title":"Breath of Life: The Respiratory Vagal Stimulation Model of Contemplative Activity","volume":"12","author":"Gerritsen","year":"2018","journal-title":"Front. Hum. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8047\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:47Z","timestamp":1760144327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":57,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208047"],"URL":"https:\/\/doi.org\/10.3390\/s22208047","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}