{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:33:27Z","timestamp":1779233607973,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Florida Center for Advanced Analytics and Data Science funded by Ernesto.Net (under the Algorithms for Good Grant)","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human\u2019s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors\u2019 knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.<\/jats:p>","DOI":"10.3390\/s21248336","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T01:22:05Z","timestamp":1639444925000},"page":"8336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2060","authenticated-orcid":false,"given":"Hafeez Ur Rehman","family":"Siddiqui","sequence":"first","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-3713","authenticated-orcid":false,"given":"Hina Fatima","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2468-8471","authenticated-orcid":false,"given":"Adil Ali","family":"Saleem","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Baqi","family":"Khan Khakwani","sequence":"additional","affiliation":[{"name":"Management and Information Technology, Jubail Industrial College, Al Jubail 35718, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-8565","authenticated-orcid":false,"given":"Ernesto","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Broward College, Broward County, FL 33301, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Dudley","sequence":"additional","affiliation":[{"name":"School of Engineering, London South Bank University, London SE1 0AA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Landowska, A. (2018). Towards New Mappings between Emotion Representation Models. Appl. Sci., 8.","DOI":"10.3390\/app8020274"},{"key":"ref_2","unstructured":"Mehrabian, A., and Russell, J.A. (1974). An Approach to Environmental Psychology, The MIT Press."},{"key":"ref_3","unstructured":"Mehrabian, A. (2021, September 25). Basic Dimensions for a General Psychological Theory Implications for Personality, Social, Environmental, and Developmental Studies. Available online: https:\/\/philpapers.org\/rec\/MEHBDF."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bales, R.F. (2017). Social Interaction Systems: Theory and Measurement, Routledge.","DOI":"10.4324\/9781315129563"},{"key":"ref_5","unstructured":"Ekman, P. (2021, September 25). Facial Expressions of Emotion: New Findings, New Questions. Available online: https:\/\/psycnet.apa.org\/record\/1992-26206-001."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/02699939208411068","article-title":"An argument for basic emotions","volume":"6","author":"Ekman","year":"1992","journal-title":"Cogn. Emot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1037\/0022-3514.53.4.712","article-title":"Universals and cultural differences in the judgments of facial expressions of emotion","volume":"53","author":"Ekman","year":"1987","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1177\/0539018405058216","article-title":"What are emotions? And how can they be measured?","volume":"44","author":"Scherer","year":"2005","journal-title":"Soc. Sci. Inf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.ijhcs.2009.03.005","article-title":"Short-term emotion assessment in a recall paradigm","volume":"67","author":"Chanel","year":"2009","journal-title":"Int. J.-Hum.-Comput. Stud."},{"key":"ref_10","first-page":"1","article-title":"Acoustic emotion recognition using linear and nonlinear cepstral coefficients","volume":"6","author":"Chenchah","year":"2015","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_11","unstructured":"Suja, P., and Tripathi, S. (2016, January 11\u201312). Real-time emotion recognition from facial images using Raspberry Pi II. Proceedings of the 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chanthaphan, N., Uchimura, K., Satonaka, T., and Makioka, T. (2015, January 23\u201327). Facial emotion recognition based on facial motion stream generated by kinect. Proceedings of the 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Bangkok, Thailand.","DOI":"10.1109\/SITIS.2015.31"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wiem, M.B.H., and Lachiri, Z. (2017, January 19\u201321). Emotion sensing from physiological signals using three defined areas in arousal-valence model. Proceedings of the 2017 International Conference on Control, Automation and Diagnosis (ICCAD), Hammamet, Tunisia.","DOI":"10.1109\/CADIAG.2017.8075660"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.inffus.2020.01.011","article-title":"Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review","volume":"59","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1016\/j.proeng.2012.06.298","article-title":"Analysis of Electroencephalography (EEG) signals and its categorization\u2014A study","volume":"38","author":"Kumar","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F.H., Osborne, T., and Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19.","DOI":"10.3390\/s19173805"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s13246-019-00825-7","article-title":"The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features","volume":"43","author":"Goshvarpour","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11571-018-9516-y","article-title":"EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences","volume":"13","author":"Goshvarpour","year":"2019","journal-title":"Cogn. Neurodyn."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Chunawale, A., and Bedekar, D. (2021, September 25). Human Emotion Recognition using Physiological Signals: A Survey. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3645402.","DOI":"10.2139\/ssrn.3645402"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0301-0511(98)00025-8","article-title":"The effects of emotional behaviour on components of the respiratory cycle","volume":"49","author":"Boiten","year":"1998","journal-title":"Biol. Psychol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.3389\/fpsyg.2020.01980","article-title":"Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation","volume":"11","author":"Jerath","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1378\/chest.125.2.683","article-title":"Respiratory sinus arrhythmia: Why does the heartbeat synchronize with respiratory rhythm?","volume":"125","author":"Yasuma","year":"2004","journal-title":"Chest"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"084001","DOI":"10.1088\/1361-6579\/ab310a","article-title":"Mutual information between heart rate variability and respiration for emotion characterization","volume":"40","author":"Valderas","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.biopsycho.2010.03.010","article-title":"Autonomic nervous system activity in emotion: A review","volume":"84","author":"Kreibig","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neulet.2012.05.047","article-title":"Remembering the past with slow breathing associated with activity in the parahippocampus and amygdala","volume":"521","author":"Masaoka","year":"2012","journal-title":"Neurosci. Lett."},{"key":"ref_28","unstructured":"Louis, E.K.S., Frey, L., Britton, J., Hopp, J., Korb, P., Koubeissi, M., Lievens, W., and Pestana-Knight, E. (2016). Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants, American Epilepsy Society."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, G., Liu, G., and Hao, M. (2010, January 28\u201329). The analysis of emotion recognition from GSR based on PSO. Proceedings of the 2010 International Symposium on Intelligence Information Processing and Trusted Computing, Huanggang, China.","DOI":"10.1109\/IPTC.2010.60"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"07TR01","DOI":"10.1088\/1361-6579\/ab299e","article-title":"Recent development of respiratory rate measurement technologies","volume":"40","author":"Liu","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Adib, F., Mao, H., Kabelac, Z., Katabi, D., and Miller, R.C. (2015). Smart homes that monitor breathing and heart rate. CHI \u201915: Proceedings of the 33rd annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18\u201323 April 2015, Association for Computing Machinery.","DOI":"10.1145\/2702123.2702200"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gong, P., Ma, H.T., and Wang, Y. (2016, January 6\u201310). Emotion recognition based on the multiple physiological signals. Proceedings of the 2016 IEEE International Conference on Real-Time Computing and Robotics (RCAR), Angkor Wat, Cambodia.","DOI":"10.1109\/RCAR.2016.7784015"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mirmohamadsadeghi, L., Yazdani, A., and Vesin, J.M. (2016, January 21\u201323). Using cardio-respiratory signals to recognize emotions elicited by watching music video clips. Proceedings of the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada.","DOI":"10.1109\/MMSP.2016.7813349"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hassani, S., Bafadel, I., Bekhatro, A., Al Blooshi, E., Ahmed, S., and Alahmad, M. (December, January 29). Physiological signal-based emotion recognition system. Proceedings of the 2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS), Salmabad, Bahrain.","DOI":"10.1109\/ICETAS.2017.8277912"},{"key":"ref_35","unstructured":"Kumar, C.N., and Shivakumar, G. (2018). A Real Time Human Emotion Recognition System Using Respiration Parameters and ECG. International Conference on Intelligent Human Computer Interaction, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wiem, M.B.H., and Lachiri, Z. (2017, January 6\u20138). Emotion recognition system based on physiological signals with Raspberry Pi III implementation. Proceedings of the 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP), Paris, France.","DOI":"10.1109\/ICFSP.2017.8097053"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rattanadoung, K., Champrasert, P., and Aramkul, S. (2018, January 1\u20133). The emotional state classification using physiological signal interpretation framework. Proceedings of the 2018 International Conference on Signals and Systems (ICSigSys), Bali, Indonesia.","DOI":"10.1109\/ICSIGSYS.2018.8373573"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hameed, R.A., Sabir, M.K., Fadhel, M.A., Al-Shamma, O., and Alzubaidi, L. (2019, January 15\u201316). Human emotion classification based on respiration signal. Proceedings of the International Conference on Information and Communication Technology, Baghdad, Iraq.","DOI":"10.1145\/3321289.3321315"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/JBHI.2019.2895589","article-title":"Human emotion characterization by heart rate variability analysis guided by respiration","volume":"23","author":"Yamuza","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.enbuild.2017.02.004","article-title":"Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles","volume":"141","author":"Brown","year":"2017","journal-title":"Energy Build."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ghavami, M., Michael, L., and Kohno, R. (2007). Ultra Wideband Signals and Systems in Communication Engineering, John Wiley & Sons.","DOI":"10.1002\/9780470060490"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rana, S.P., Dey, M., Siddiqui, H.U., Tiberi, G., Ghavami, M., and Dudley, S. (2017, January 12\u201315). UWB localization employing supervised learning method. Proceedings of the 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB), Salamanca, Spain.","DOI":"10.1109\/ICUWB.2017.8250971"},{"key":"ref_43","unstructured":"Dudley, S., Rana, S., Dey, M., Brown, R., and Siddiqui, H. (2018, January 9\u201313). Remote Vital Sign Recognition Through Machine Learning Augmented UWB. Proceedings of the European Conference on Antennas and Propagation, London, UK."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Siddiqui, H.U.R., Saleem, A.A., Brown, R., Bademci, B., Lee, E., Rustam, F., and Dudley, S. (2021). Non-invasive driver drowsiness detection system. Sensors, 21.","DOI":"10.3390\/s21144833"},{"key":"ref_45","unstructured":"Novelda (2021, September 25). Novelda X4. Available online: https:\/\/novelda.com\/x4-soc.html."},{"key":"ref_46","unstructured":"Laonuri (2021, September 25). X4M300 Datasheet. Available online: http:\/\/laonuri.techyneeti.com\/wp-content\/uploads\/2019\/02\/X4M300_DATASHEET.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, D.H. (2020). Lane detection method with impulse radio ultra-wideband radar and metal lane reflectors. Sensors, 20.","DOI":"10.3390\/s20010324"},{"key":"ref_48","unstructured":"Novelda (2021, September 25). X4-Datasheet. Available online: https:\/\/novelda.com\/content\/wp-content\/uploads\/2021\/01\/NOVELDA-x4-datasheet-revF.pdf."},{"key":"ref_49","first-page":"1","article-title":"A novel non-contact heart rate monitor using impulse-radio ultra-wideband (IR-UWB) radar technology","volume":"8","author":"Lee","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.medengphy.2017.09.003","article-title":"Non-contact and through-clothing measurement of the heart rate using ultrasound vibrocardiography","volume":"50","author":"Gateau","year":"2017","journal-title":"Med. Eng. Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep37212","article-title":"Resting-state high-frequency heart rate variability is related to respiratory frequency in individuals with severe mental illness but not healthy controls","volume":"6","author":"Quintana","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/BF00952248","article-title":"Respiratory control of heart rate","volume":"50","author":"Ahmed","year":"1982","journal-title":"Eur. J. Appl. Physiol. Occup. Physiol."},{"key":"ref_53","unstructured":"Tiinanen, S., Kiviniemi, A., Tulppo, M., and Sepp\u00e4nen, T. (2010, January 26\u201329). RSA component extraction from cardiovascular signals by combining adaptive filtering and PCA derived respiration. Proceedings of the 2010 Computing in Cardiology, Belfast, UK."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1515\/cdbme-2015-0012","article-title":"Separating the effect of respiration from the heart rate variability for cases of constant harmonic breathing","volume":"1","author":"Kircher","year":"2015","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_55","unstructured":"Yeh, S.T. Using trapezoidal rule for the area under a curve calculation. Proceedings of the 27th Annual SAS\u00ae User Group International (SUGI\u201902), Orlando, FL, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"218898","DOI":"10.1109\/ACCESS.2020.3041822","article-title":"Sensor-based human activity recognition using deep stacked multilayered perceptron model","volume":"8","author":"Rustam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6621607","DOI":"10.1155\/2021\/6621607","article-title":"An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification","volume":"2021","author":"Reshi","year":"2021","journal-title":"Complexity"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"78621","DOI":"10.1109\/ACCESS.2021.3083638","article-title":"Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification using RVVC Model","volume":"9","author":"Rupapara","year":"2021","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"100404","DOI":"10.1016\/j.ascom.2020.100404","article-title":"Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC)","volume":"32","author":"Rustam","year":"2020","journal-title":"Astron. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:46:56Z","timestamp":1760168816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,13]]},"references-count":59,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248336"],"URL":"https:\/\/doi.org\/10.3390\/s21248336","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,13]]}}}