{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:42:40Z","timestamp":1775788960626,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:00Z","timestamp":1604966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00742\/2020"],"award-info":[{"award-number":["UIDB\/00742\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data.<\/jats:p>","DOI":"10.3390\/fi12110194","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T14:10:41Z","timestamp":1605017441000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-6762","authenticated-orcid":false,"given":"Ivan Miguel","family":"Pires","sequence":"first","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6200-001 Covilh\u00e3, Portugal"},{"name":"Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal"},{"name":"UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9812-7488","authenticated-orcid":false,"given":"Faisal","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Engineering and Technology (UET), Taxila 47080, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-3168","authenticated-orcid":false,"given":"Nuno M.","family":"M. Garcia","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6200-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5336-1796","authenticated-orcid":false,"given":"Petre","family":"Lameski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-0168","authenticated-orcid":false,"given":"Eftim","family":"Zdravevski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hung, W.-C., Shen, F., Wu, Y.-L., Hor, M.-K., and Tang, C.-Y. (2014, January 13\u201316). Activity Recognition with sensors on mobile devices. Proceedings of the 2014 International Conference on Machine Learning and Cybernetics, Lanzhou, China.","DOI":"10.1109\/ICMLC.2014.7009650"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T.S., Kj\u00e6rgaard, M.B., Dey, A., Sonne, T., and Jensen, M.M. (2015, January 1\u20134). Smart Devices are Different: Assessing and Mitigating: Mobile Sensing Heterogeneities for Activity Recognition. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems\u2014SenSys \u201915, Seoul, Korea.","DOI":"10.1145\/2809695.2809718"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Adans-Dester, C., Bamberg, S., Bertacchi, F., Caulfield, B., Chappie, K., Demarchi, D., Erb, M.K., Estrada, J., Fabara, E., and Freni, M. (2020). Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?. IEEE Open J. Eng. Med. Biol., 1.","DOI":"10.1109\/OJEMB.2020.3015141"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zeinalipour-Yazti, D., and Claramunt, C. (July, January 30). COVID-19 Mobile Contact Tracing Apps (MCTA): A Digital Vaccine or a Privacy Demolition?. Proceedings of the 2020 21st IEEE International Conference on Mobile Data Management (MDM), Versailles, France.","DOI":"10.1109\/MDM48529.2020.00020"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/JSEN.2011.2146246","article-title":"Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls","volume":"12","author":"Shany","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/03093640600983949","article-title":"Clinical applications of sensors for human posture and movement analysis: A review","volume":"31","author":"Wong","year":"2007","journal-title":"Prosthet. Orthot. Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1109\/ACCESS.2017.2676168","article-title":"Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","article-title":"Real-time human activity recognition from accelerometer data using Convolutional Neural Networks","volume":"62","author":"Ignatov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Chen, W. (2016, January 27\u201329). MEMS-based human activity recognition using smartphone. Proceedings of the 2016 35th Chinese Control Conference (CCC), Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7553975"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Chen, Z., and Soh, Y.C. (2015, January 15\u201317). Smartphone-based Human Activity Recognition in buildings using Locality-constrained Linear Coding. Proceedings of the 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand.","DOI":"10.1109\/ICIEA.2015.7334113"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3233\/AIS-140270","article-title":"An integrated home-based self-management system to support the wellbeing of older adults","volume":"6","author":"Doyle","year":"2014","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zainal, A., Razak, F.H.A., and Ahmad, N.A. (2013, January 23\u201324). Older People and the Use of Mobile Phones: An Interview Study. Proceedings of the 2013 International Conference on Advanced Computer Science Applications and Technologies, Kuching, Malaysia.","DOI":"10.1109\/ACSAT.2013.83"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"309","DOI":"10.2147\/CIA.S157911","article-title":"Feasibility of a community-based Functional Power Training program for older adults","volume":"13","author":"Tan","year":"2018","journal-title":"Clin. Interv. Aging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1111\/apa.15067","article-title":"Impact of the global financial crisis on newborn care in Portugal and Spain: Perception of health professionals","volume":"109","author":"Boix","year":"2020","journal-title":"Acta Paediatr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.nedt.2010.12.023","article-title":"Emotional intelligence: Its relationship to stress, coping, well-being and professional performance in nursing students","volume":"31","author":"Por","year":"2011","journal-title":"Nurse Educ. Today"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"283rv3","DOI":"10.1126\/scitranslmed.aaa3487","article-title":"The emerging field of mobile health","volume":"7","author":"Steinhubl","year":"2015","journal-title":"Sci. Transl. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sendra, S., Granell, E., Lloret, J., and Rodrigues, J.J.P.C. (2012, January 10\u201315). Smart collaborative system using the sensors of mobile devices for monitoring disabled and elderly people. Proceedings of the 2012 IEEE International Conference on Communications (ICC), Ottawa, ON, Canada.","DOI":"10.1109\/ICC.2012.6364935"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Majumder, S., and Deen, M.J. (2019). Smartphone Sensors for Health Monitoring and Diagnosis. Sensors, 19.","DOI":"10.3390\/s19092164"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1088\/0967-3334\/36\/9\/1929","article-title":"Clinical frailty syndrome assessment using inertial sensors embedded in smartphones","volume":"36","year":"2015","journal-title":"Physiol. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Glowinski, S., \u0141osi\u0144ski, K., Kowia\u0144ski, P., Wa\u015bkow, M., Bryndal, A., and Grochulska, A. (2020). Inertial Sensors as a Tool for Diagnosing Discopathy Lumbosacral Pathologic Gait: A Preliminary Research. Diagnostics, 10.","DOI":"10.3390\/diagnostics10060342"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.medengphy.2011.05.002","article-title":"Activity classification using a single chest mounted tri-axial accelerometer","volume":"33","author":"Godfrey","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Stankevich, E., Paramonov, I., and Timofeev, I. (2012, January 5\u20139). Mobile phone sensors in health applications. Proceedings of the 2012 12th Conference of Open Innovations Association (FRUCT), Oulu, Finland.","DOI":"10.23919\/FRUCT.2012.8122097"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sousa, P., Sabugueiro, D., Felizardo, V., Couto, R., Pires, I., and Garcia, N. (2015). mHealth sensors and applications for personal aid. Mobile Health, Springer.","DOI":"10.1007\/978-3-319-12817-7_12"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Amoh, J., and Odame, K.M. (2014). Technologies for Developing Ambulatory Cough Monitoring Devices. Crit. Rev. Biomed. Eng.","DOI":"10.1615\/CritRevBiomedEng.2014010886"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/2049-3258-72-28","article-title":"Smart wearable body sensors for patient self-assessment and monitoring","volume":"72","author":"Appelboom","year":"2014","journal-title":"Arch Public Health"},{"key":"ref_26","first-page":"1","article-title":"A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors","volume":"2015","author":"Kakria","year":"2015","journal-title":"Int. J. Telemed. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1007\/s10916-016-0497-2","article-title":"Smartphone-Based Patients\u2019 Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring","volume":"40","author":"Guo","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMCC.2009.2032660","article-title":"A survey on wearable sensor-based systems for health monitoring and prognosis","volume":"40","author":"Pantelopoulos","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Valente, T., Pombo, N., and Garcia, N.M. (2018). Conceptual Definition of a Platform for the Monitoring of the Subjects with Nephrolithiasis Based on the Energy Expenditure and the Activities of Daily Living Performed. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection, Springer.","DOI":"10.1007\/978-3-319-94779-2_1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/978-3-642-11745-9_2","article-title":"Intelligent Mobile Health Monitoring System (IMHMS)","volume":"Volume 27","author":"Kostkova","year":"2010","journal-title":"Electronic Healthcare"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pires, I., Felizardo, V., Pombo, N., and Garcia, N.M. (2017, January 17\u201321). Limitations of energy expenditure calculation based on a mobile phone accelerometer. Proceedings of the 2017 International Conference on High Performance Computing & Simulation (HPCS), Genoa, Italy.","DOI":"10.1109\/HPCS.2017.29"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Garcia, N.M., Pombo, N., and Fl\u00f3rez-Revuelta, F. (2018). Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People. Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), Science and Technology Publications, Lda.","DOI":"10.5220\/0006817802690275"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Patro, S.G.K., and Sahu, K.K. (2015). Normalization: A Preprocessing Stage. Int. Adv. Res. J. Sci. Eng. Technol., 20\u201322.","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mistry, J., and Inden, B. (2018, January 19\u201321). An Approach to Sign Language Translation using the Intel RealSense Camera. Proceedings of the 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, UK.","DOI":"10.1109\/CEEC.2018.8674227"},{"key":"ref_35","unstructured":"Narkhede, A.H. (2019). Human Activity Recognition Based on Multimodal Body Sensing. [Master\u2019s Thesis, San Jose State University]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vermeulen, A.F. (2020). Unsupervised Learning: Deep Learning. Industrial Machine Learning, Apress.","DOI":"10.1007\/978-1-4842-5316-8"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1007\/978-3-319-25783-9_41","article-title":"SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fitting","volume":"Volume 9437","author":"Yao","year":"2015","journal-title":"Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"Jakubczyk","year":"2018","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class AdaBoost","volume":"2","author":"Hastie","year":"2009","journal-title":"Stat. Interface"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hassoun, M.H. (1995). Fundamentals of Artificial Neural Networks, MIT Press.","DOI":"10.1109\/JPROC.1996.503146"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/BF00116835","article-title":"The CN2 induction algorithm","volume":"3","author":"Clark","year":"1989","journal-title":"Mach Learn"},{"key":"ref_44","unstructured":"Pfaff, F., Noack, B., and Hanebeck, U.D. (2013, January 9\u201312). Data validation in the presence of stochastic and set-membership uncertainties. Proceedings of the Information Fusion (FUSION), 2013 16th International Conference on Information Fusion, Istanbul, Turkey."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"61","DOI":"10.2174\/1875036201811010061","article-title":"Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions","volume":"11","author":"Pires","year":"2018","journal-title":"Open Bioinform. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.pmcj.2018.05.005","article-title":"Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices","volume":"47","author":"Pires","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Garcia, N.M., Pombo, N., and Fl\u00f3rez-Revuelta, F. (2016). From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices. Sensors, 16.","DOI":"10.3390\/s16020184"},{"key":"ref_48","unstructured":"(2020, August 29). Smartphones BQ Aquaris | BQ Portugal. Available online: https:\/\/www.bq.com\/pt\/smartphones."},{"key":"ref_49","unstructured":"(2020, September 17). BQ Aquaris 5.7\u2014Specifications. Available online: https:\/\/www.devicespecifications.com\/en\/model\/59bb30eb."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Hussain, F., Garcia, N.M., and Zdravevski, E. (2020). Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study. Future Internet, 12.","DOI":"10.3390\/fi12090155"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Marques, G., Garcia, N.M., Fl\u00f3rez-Revuelta, F., Canavarro Teixeira, M., Zdravevski, E., Spinsante, S., and Coimbra, M. (2020). Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer. Electronics, 9.","DOI":"10.3390\/electronics9030509"},{"key":"ref_52","unstructured":"Pires, I. (2020). Raw dataset with accelerometer, gyroscope and magnetometer data for activities with motion. Mendeley."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wan, X., Wang, W., Liu, J., and Tong, T. (2014). Estimating the sample mean and standard deviation from the sample size, median, range and\/or interquartile range. BMC Med. Res. Methodol., 14.","DOI":"10.1186\/1471-2288-14-135"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ferreira, J.M., Pires, I.M., Marques, G., Garc\u00eda, N.M., Zdravevski, E., Lameski, P., Fl\u00f3rez-Revuelta, F., Spinsante, S., and Xu, L. (2020). Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study. Electronics, 9.","DOI":"10.3390\/electronics9010180"},{"key":"ref_55","unstructured":"Ng, A.Y. (2004, January 4\u20138). Feature Selection, L1 vs. L2 Regularization, and Rotational Invariance. Proceedings of the Twenty-First International Conference on Machine Learning\u2014ICML \u201904, Banff, AL, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zdravevski, E., Lameski, P., Mingov, R., Kulakov, A., and Gjorgjevikj, D. (2015, January 13\u201316). Robust Histogram-Based Feature Engineering of Time Series Data. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland.","DOI":"10.15439\/2015F420"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zdravevski, E., Lameski, P., and Kulakov, A. (2016, January 11\u201314). Automatic feature engineering for prediction of dangerous seismic activities in coal mines. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland.","DOI":"10.15439\/2016F152"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Santos, R., Pombo, N., Garcia, N.M., Fl\u00f3rez-Revuelta, F., Spinsante, S., Goleva, R., and Zdravevski, E. (2018). Recognition of activities of daily living based on environmental analyses using audio fingerprinting techniques: A systematic review. Sensors, 18.","DOI":"10.3390\/s18010160"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:31:51Z","timestamp":1760178711000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,10]]},"references-count":58,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["fi12110194"],"URL":"https:\/\/doi.org\/10.3390\/fi12110194","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,10]]}}}