{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:24:47Z","timestamp":1773779087049,"version":"3.50.1"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031349591","type":"print"},{"value":"9783031349607","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34960-7_35","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T15:04:08Z","timestamp":1687964648000},"page":"492-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Radar Sensing in Healthcare: Challenges and Achievements in\u00a0Human Activity Classification & Vital Signs Monitoring"],"prefix":"10.1007","author":[{"given":"Francesco","family":"Fioranelli","sequence":"first","affiliation":[]},{"given":"Ronny G.","family":"Guendel","sequence":"additional","affiliation":[]},{"given":"Nicolas C.","family":"Kruse","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Yarovoy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"issue":"1","key":"35_CR1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/JMW.2020.3033616","volume":"1","author":"C Waldschmidt","year":"2021","unstructured":"Waldschmidt, C., Hasch, J., Menzel, W.: Automotive radar - from first efforts to future systems. IEEE J. Microwaves 1(1), 135\u2013148 (2021)","journal-title":"IEEE J. Microwaves"},{"issue":"4","key":"35_CR2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MSP.2020.2978507","volume":"37","author":"S Sun","year":"2020","unstructured":"Sun, S., Petropulu, A.P., Poor, H.V.: MIMO radar for advanced driver-assistance systems and autonomous driving: advantages and challenges. IEEE Signal Process. Mag. 37(4), 98\u2013117 (2020)","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"35_CR3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MPOT.2019.2906977","volume":"38","author":"F Fioranelli","year":"2019","unstructured":"Fioranelli, F., Le Kernec, J., Shah, S.A.: Radar for health care: recognizing human activities and monitoring vital signs. IEEE Potentials 38(4), 16\u201323 (2019)","journal-title":"IEEE Potentials"},{"issue":"4","key":"35_CR4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MSP.2019.2903715","volume":"36","author":"J Le Kernec","year":"2019","unstructured":"Le Kernec, J., et al.: Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms. IEEE Signal Process. Mag. 36(4), 29\u201341 (2019)","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"35_CR5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MSP.2018.2890128","volume":"36","author":"SZ Gurbuz","year":"2019","unstructured":"Gurbuz, S.Z., Amin, M.G.: Radar-based human-motion recognition with deep learning: promising applications for indoor monitoring. IEEE Signal Process. Mag. 36(4), 16\u201328 (2019)","journal-title":"IEEE Signal Process. Mag."},{"issue":"12","key":"35_CR6","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1109\/JSEN.2017.2697077","volume":"17","author":"E Cippitelli","year":"2017","unstructured":"Cippitelli, E., Fioranelli, F., Gambi, E., Spinsante, S.: Radar and RGB-depth sensors for fall detection: a review. IEEE Sens. J. 17(12), 3585\u20133604 (2017)","journal-title":"IEEE Sens. J."},{"issue":"2","key":"35_CR7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JETCAS.2018.2837778","volume":"8","author":"C Li","year":"2018","unstructured":"Li, C., Mak, P.-I., G\u00f3mez-Garc\u00eda, R., Chen, Y.: Guest editorial wireless sensing circuits and systems for healthcare and biomedical applications. IEEE J. Emerg. Select. Top. Circuits Syst. 8(2), 161\u2013164 (2018)","journal-title":"IEEE J. Emerg. Select. Top. Circuits Syst."},{"issue":"22","key":"35_CR8","doi-asserted-by":"crossref","first-page":"25870","DOI":"10.1109\/JSEN.2021.3117707","volume":"21","author":"S Iwata","year":"2021","unstructured":"Iwata, S., Koda, T., Sakamoto, T.: Multiradar data fusion for respiratory measurement of multiple people. IEEE Sens. J. 21(22), 25870\u201325879 (2021)","journal-title":"IEEE Sens. J."},{"key":"35_CR9","doi-asserted-by":"crossref","first-page":"106895","DOI":"10.1109\/ACCESS.2021.3099821","volume":"9","author":"T Koda","year":"2021","unstructured":"Koda, T., Sakamoto, T., Okumura, S., Taki, H.: Noncontact respiratory measurement for multiple people at arbitrary locations using array radar and respiratory-space clustering. IEEE Access 9, 106895\u2013106906 (2021)","journal-title":"IEEE Access"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Paterniani, G., et al.: Radar-based monitoring of vital signs: a tutorial overview. In: Proceedings of the IEEE, pp. 1\u201341 (2023)","DOI":"10.36227\/techrxiv.19212918.v1"},{"issue":"7","key":"35_CR11","doi-asserted-by":"crossref","first-page":"9128","DOI":"10.1109\/JSEN.2021.3052602","volume":"21","author":"Y Oyamada","year":"2021","unstructured":"Oyamada, Y., Koshisaka, T., Sakamoto, T.: Experimental demonstration of accurate noncontact measurement of arterial pulse wave displacements using 79-GHZ array radar. IEEE Sens. J. 21(7), 9128\u20139137 (2021)","journal-title":"IEEE Sens. J."},{"issue":"8","key":"35_CR12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MMM.2019.2915469","volume":"20","author":"H Hong","year":"2019","unstructured":"Hong, H., et al.: Microwave sensing and sleep: noncontact sleep-monitoring technology with microwave biomedical radar. IEEE Microw. Mag. 20(8), 18\u201329 (2019)","journal-title":"IEEE Microw. Mag."},{"key":"35_CR13","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1038\/s41598-020-62061-4","volume":"10","author":"S Kang","year":"2020","unstructured":"Kang, S., et al.: Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar. Sci. Rep. 10, 5261 (2020)","journal-title":"Sci. Rep."},{"issue":"5","key":"35_CR14","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TGRS.2009.2012849","volume":"47","author":"Y Kim","year":"2009","unstructured":"Kim, Y., Ling, H.: Human activity classification based on micro-doppler signatures using a support vector machine. IEEE Trans. Geosci. Remote Sens. 47(5), 1328\u20131337 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"35_CR15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/MSP.2015.2502784","volume":"33","author":"MG Amin","year":"2016","unstructured":"Amin, M.G., Zhang, Y.D., Ahmad, F., Ho, K.D.: Radar signal processing for elderly fall detection: the future for in-home monitoring. IEEE Signal Process. Mag. 33(2), 71\u201380 (2016)","journal-title":"IEEE Signal Process. Mag."},{"key":"35_CR16","first-page":"1","volume":"60","author":"X Li","year":"2022","unstructured":"Li, X., He, Y., Fioranelli, F., Jing, X.: Semisupervised human activity recognition with radar micro-doppler signatures. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR17","doi-asserted-by":"crossref","first-page":"85334","DOI":"10.1109\/ACCESS.2021.3088452","volume":"9","author":"A Gorji","year":"2021","unstructured":"Gorji, A., Bourdoux, A., Sahli, H.: On the generalization and reliability of single radar-based human activity recognition. IEEE Access 9, 85334\u201385349 (2021)","journal-title":"IEEE Access"},{"issue":"4","key":"35_CR18","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/JBHI.2020.3025900","volume":"25","author":"M Piriyajitakonkij","year":"2021","unstructured":"Piriyajitakonkij, M., et al.: SleepPoseNet: multi-view learning for sleep postural transition recognition using UWB. IEEE J. Biomed. Health Inform. 25(4), 1305\u20131314 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"35_CR19","first-page":"1","volume":"60","author":"S Zhu","year":"2022","unstructured":"Zhu, S., Guendel, R.G., Yarovoy, A., Fioranelli, F.: Continuous human activity recognition with distributed radar sensor networks and CNN-RNN architectures. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"35_CR20","doi-asserted-by":"crossref","first-page":"12173","DOI":"10.1109\/JSEN.2022.3175618","volume":"22","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Yarovoy, A., Fioranelli, F.: Angle-insensitive human motion and posture recognition based on 4D imaging radar and deep learning classifiers. IEEE Sens. J. 22(12), 12173\u201312182 (2022)","journal-title":"IEEE Sens. J."},{"issue":"12","key":"35_CR21","doi-asserted-by":"crossref","first-page":"13522","DOI":"10.1109\/JSEN.2021.3068388","volume":"21","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Alnujaim, I., Oh, D.: Human activity classification based on point clouds measured by millimeter wave MIMO radar with deep recurrent neural networks. IEEE Sens. J. 21(12), 13522\u201313529 (2021)","journal-title":"IEEE Sens. J."},{"issue":"9","key":"35_CR22","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1109\/TBME.2014.2319333","volume":"61","author":"F Wang","year":"2014","unstructured":"Wang, F., Skubic, M., Rantz, M., Cuddihy, P.E.: Quantitative gait measurement with pulse-doppler radar for passive in-home gait assessment. IEEE Trans. Biomed. Eng. 61(9), 2434\u20132443 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"9","key":"35_CR23","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1109\/TBME.2019.2893528","volume":"66","author":"A-K Seifert","year":"2019","unstructured":"Seifert, A.-K., Amin, M.G., Zoubir, A.M.: Toward unobtrusive in-home gait analysis based on radar micro-doppler signatures. IEEE Trans. Biomed. Eng. 66(9), 2629\u20132640 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"35_CR24","doi-asserted-by":"crossref","first-page":"7590","DOI":"10.1109\/JSEN.2020.3046991","volume":"21","author":"H Li","year":"2021","unstructured":"Li, H., Mehul, A., Le Kernec, J., Gurbuz, S.Z., Fioranelli, F.: Sequential human gait classification with distributed radar sensor fusion. IEEE Sens. J. 21(6), 7590\u20137603 (2021)","journal-title":"IEEE Sens. J."},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Gurbuz, S.Z., Rahman, M.M., Kurtoglu, E., Martelli, D.: Continuous human activity recognition and step-time variability analysis with FMCW radar. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 01\u201304 (2022)","DOI":"10.1109\/BHI56158.2022.9926892"},{"issue":"3","key":"35_CR26","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/JSEN.2016.2628099","volume":"17","author":"K Chaccour","year":"2017","unstructured":"Chaccour, K., Darazi, R., El Hassani, A.H., Andr\u00e8s, E.: From fall detection to fall prevention: a generic classification of fall-related systems. IEEE Sens. J. 17(3), 812\u2013822 (2017)","journal-title":"IEEE Sens. J."},{"issue":"2","key":"35_CR27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MSP.2015.2503881","volume":"33","author":"C Debes","year":"2016","unstructured":"Debes, C., Merentitis, A., Sukhanov, S., Niessen, M., Frangiadakis, N., Bauer, A.: Monitoring activities of daily living in smart homes: understanding human behavior. IEEE Signal Process. Mag. 33(2), 81\u201394 (2016)","journal-title":"IEEE Signal Process. Mag."},{"issue":"5","key":"35_CR28","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TMTT.2017.2650911","volume":"65","author":"C Li","year":"2017","unstructured":"Li, C., et al.: A review on recent progress of portable short-range noncontact microwave radar systems. IEEE Trans. Microw. Theory Tech. 65(5), 1692\u20131706 (2017)","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Fioranelli, F., Griffiths, H., Ritchie, M., Balleri, A.: Micro-Doppler Radar and Its Applications. Institution of Engineering and Technology (2020). https:\/\/digital-library.theiet.org\/content\/books\/ra\/sbra531e","DOI":"10.1049\/SBRA531E"},{"key":"35_CR30","volume-title":"The Micro-Doppler Effect in Radar","author":"V Chen","year":"2019","unstructured":"Chen, V.: The Micro-Doppler Effect in Radar, 2nd edn. Artech, New Jersey (2019)","edition":"2"},{"key":"35_CR31","doi-asserted-by":"crossref","unstructured":"Gurbuz, S.Z.: Deep Neural Network Design for Radar Applications. Institution of Engineering and Technology (2020). https:\/\/digital-library.theiet.org\/content\/books\/ra\/sbra529e","DOI":"10.1049\/SBRA529E"},{"issue":"6","key":"35_CR32","doi-asserted-by":"crossref","first-page":"7918","DOI":"10.1109\/JSEN.2020.3044675","volume":"21","author":"RG Guendel","year":"2021","unstructured":"Guendel, R.G., Fioranelli, F., Yarovoy, A.: Phase-based classification for arm gesture and gross-motor activities using histogram of oriented gradients. IEEE Sens. J. 21(6), 7918\u20137927 (2021)","journal-title":"IEEE Sens. J."},{"key":"35_CR33","doi-asserted-by":"crossref","unstructured":"Scarnati, T., Lewis, B.: Complex-valued neural networks for synthetic aperture radar image classification. In: 2021 IEEE Radar Conference (RadarConf21), pp. 1\u20136 (2021)","DOI":"10.1109\/RadarConf2147009.2021.9455316"},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Brooks, D.A., Schwander, O., Barbaresco, F., Schneider, J.Y., Cord, M.: Complex-valued neural networks for fully-temporal micro-doppler classification. In: 2019 20th International Radar Symposium (IRS), pp. 1\u201310 (2019)","DOI":"10.23919\/IRS.2019.8768161"},{"key":"35_CR35","doi-asserted-by":"crossref","unstructured":"Yang, X., Guendel, R.G., Yarovoy, A., Fioranelli, F.: Radar-based human activities classification with complex-valued neural networks. In: 2022 IEEE Radar Conference (RadarConf22), pp. 1\u20136 (2022)","DOI":"10.1109\/RadarConf2248738.2022.9763903"},{"issue":"22","key":"35_CR36","doi-asserted-by":"crossref","first-page":"13607","DOI":"10.1109\/JSEN.2020.3006386","volume":"20","author":"A Shrestha","year":"2020","unstructured":"Shrestha, A., Li, H., Le Kernec, J., Fioranelli, F.: Continuous human activity classification from FMCW radar with Bi-LSTM networks. IEEE Sens. J. 20(22), 13607\u201313619 (2020)","journal-title":"IEEE Sens. J."},{"issue":"18","key":"35_CR37","doi-asserted-by":"crossref","first-page":"20335","DOI":"10.1109\/JSEN.2021.3094548","volume":"21","author":"P Chen","year":"2021","unstructured":"Chen, P., et al.: Multi-view real-time human motion recognition based on ensemble learning. IEEE Sens. J. 21(18), 20335\u201320347 (2021)","journal-title":"IEEE Sens. J."},{"issue":"3","key":"35_CR38","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","volume":"20","author":"H Li","year":"2020","unstructured":"Li, H., Shrestha, A., Heidari, H., Le Kernec, J., Fioranelli, F.: Bi-LSTM network for multimodal continuous human activity recognition and fall detection. IEEE Sens. J. 20(3), 1191\u20131201 (2020)","journal-title":"IEEE Sens. J."},{"key":"35_CR39","doi-asserted-by":"crossref","unstructured":"Kruse, N., Guendel, R., Fioranelli, F., Yarovoy, A.: Segmentation of micro-doppler signatures of human sequential activities using r\u00e9nyi entropy. In: International Conference on Radar Systems (RADAR 2022), vol. 2022, pp. 435\u2013440 (2022)","DOI":"10.1049\/icp.2022.2357"},{"issue":"4","key":"35_CR40","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TAES.2018.2799758","volume":"54","author":"MS Seyfio\u011flu","year":"2018","unstructured":"Seyfio\u011flu, M.S., \u00d6zbayo\u011flu, A.M., G\u00fcrb\u00fcz, S.Z.: Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Trans. Aerosp. Electron. Syst. 54(4), 1709\u20131723 (2018)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"issue":"4","key":"35_CR41","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TAES.2020.2969579","volume":"56","author":"B Erol","year":"2020","unstructured":"Erol, B., Gurbuz, S.Z., Amin, M.G.: Motion classification using kinematically sifted ACGAN-synthesized radar micro-doppler signatures. IEEE Trans. Aerosp. Electron. Syst. 56(4), 3197\u20133213 (2020)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"35_CR42","unstructured":"Rahman, M.M., Gurbuz, S.Z., Amin, M.G.: Physics-aware generative adversarial networks for radar-based human activity recognition. IEEE Transactions on Aerospace and Electronic Systems, pp. 1\u201315 (2022)"},{"key":"35_CR43","doi-asserted-by":"crossref","unstructured":"Zheng, L., et al.: Dynamic hand gesture recognition in in-vehicle environment based on FMCW radar and transformer. Sensors, 21(19), 6368 (2021). https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6368","DOI":"10.3390\/s21196368"},{"key":"35_CR44","doi-asserted-by":"crossref","unstructured":"Chen, S., He, W., Ren, J., Jiang, X.: Attention-based dual-stream vision transformer for radar gait recognition. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3668\u20133672 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746565"},{"key":"35_CR45","doi-asserted-by":"crossref","unstructured":"Guo, Z., Guendel, R., Yarovoy, A., Fioranelli, F.: Point transformer based human activity recognition using high dimensional radar point clouds. In: Accepted for IEEE Radar Conference 2023, San Antonio, TX, USA (2023)","DOI":"10.1109\/RadarConf2351548.2023.10149679"},{"issue":"12","key":"35_CR46","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MAES.2022.3216262","volume":"37","author":"F Fioranelli","year":"2022","unstructured":"Fioranelli, F., Zhu, S., Roldan, I.: Benchmarking classification algorithms for radar-based human activity recognition. IEEE Aerosp. Electron. Syst. Mag. 37(12), 37\u201340 (2022)","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"35_CR47","doi-asserted-by":"crossref","unstructured":"Guendel, R.G., Unterhorst, M., Fioranelli, F., Yarovoy, A.: Dataset of continuous human activities performed in arbitrary directions collected with a distributed radar network of five nodes (2021). https:\/\/data.4tu.nl\/articles\/dataset\/Dataset_of_continuous_human_activities_performed_in_arbitrary_directions_collected_with_a_distributed_radar_network_of_five_nodes\/16691500","DOI":"10.1109\/RadarConf2147009.2021.9454972"},{"key":"35_CR48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JBHI.2022.3229941","volume":"27","author":"S Yang","year":"2023","unstructured":"Yang, S., et al.: The human activity radar challenge: benchmarking based on the \u2018radar signatures of human activities\u2019 dataset from Glasgow university. IEEE J. Biomed. Health Inform. 27, 1\u201313 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"35_CR49","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.patcog.2018.07.030","volume":"85","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Hou, C., Lang, Y., Guan, D., Huang, D., Xu, J.: Open-set human activity recognition based on micro-doppler signatures. Pattern Recogn. 85, 60\u201369 (2019)","journal-title":"Pattern Recogn."},{"key":"35_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3235243","volume":"20","author":"G Bhavanasi","year":"2023","unstructured":"Bhavanasi, G., Werthen-Brabants, L., Dhaene, T., Couckuyt, I.: Open-set patient activity recognition with radar sensors and deep learning. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Lecture Notes in Computer Science","Bioinformatics and Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34960-7_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T15:07:58Z","timestamp":1687964878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34960-7_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031349591","9783031349607"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34960-7_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWBBIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Bioinformatics and Biomedical Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Meloneras, Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwbbio2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwbbio.ugr.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"203","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}