{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:49:31Z","timestamp":1779194971511,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids \u201creinventing the wheel,\u201d but also presents a lightweight solution to otherwise computationally heavy problems.<\/jats:p>","DOI":"10.3390\/info12020063","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"63","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9111-7305","authenticated-orcid":false,"given":"Fatima Sajid","family":"Butt","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Frankfurt University of Applied Sciences, D-60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-0452","authenticated-orcid":false,"given":"Luigi","family":"La Blunda","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Frankfurt University of Applied Sciences, D-60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8702-9257","authenticated-orcid":false,"given":"Matthias F.","family":"Wagner","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Frankfurt University of Applied Sciences, D-60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4797-0306","authenticated-orcid":false,"given":"J\u00f6rg","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Frankfurt University of Applied Sciences, D-60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7543-2671","authenticated-orcid":false,"given":"Inmaculada","family":"Medina-Bulo","sequence":"additional","affiliation":[{"name":"Escuela Superior de Ingenier\u00eda, Universidad de C\u00e1diz, 11001 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6890-6584","authenticated-orcid":false,"given":"David","family":"G\u00f3mez-Ullate","sequence":"additional","affiliation":[{"name":"Escuela Superior de Ingenier\u00eda, Universidad de C\u00e1diz, 11001 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2007). WHO Global Report on Falls Prevention in Older Age, WHO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"M458","DOI":"10.1093\/gerona\/55.8.M458","article-title":"Diagnostic yield and development of a Neuro cardiovascular investigation unit for older adults in a district hospital","volume":"55","author":"Allcock","year":"2000","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1111\/j.1532-5415.1987.tb04341.x","article-title":"Factors Associated with Serious Injury During Falls by Ambulatory Nursing Home Residents","volume":"35","author":"Tinetti","year":"1987","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1136\/bmj.282.6260.266","article-title":"How dangerous are falls in old people at home?","volume":"282","author":"Wild","year":"1981","journal-title":"Br. Med. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ramamoorthy, V., Gal, U., and Guez, A. (2020). Possible Life Saver: A Review on Human Fall Detection Technology. Robotics, 9.","DOI":"10.3390\/robotics9030055"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Genoud, D., Cuendet, V., and Torrent, J. (2016, January 23\u201325). Soft Fall Detection Using Machine Learning in Wearable Devices. Proceedings of the 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland.","DOI":"10.1109\/AINA.2016.124"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/JBHI.2018.2808281","article-title":"Deep Learning for Fall Detection: Three-Dimensional CNN Combined with LSTM on Video Kinematic Data","volume":"23","author":"Lu","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., and Maglogiannis, I. (2018). A Survey on Deep Transfer Learning. Artificial Neural Networks and Machine Learning\u2014 ICANN 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01418-6"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhou, Y., and Zhu, J. (2018). New Advances and Challenges of Fall Detection Systems: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8030418"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s12652-017-0592-3","article-title":"Fall detection monitoring systems: A comprehensive review","volume":"9","author":"Vallabh","year":"2018","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17195","DOI":"10.3390\/s150717195","article-title":"Robust Indoor Human Activity Recognition Using Wireless Signals","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Damodaran, N., and Sch\u00e4fer, J. (2019, January 19\u201323). Device Free Human Activity Recognition using WiFi Channel State Information. Proceedings of the 2019 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), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00205"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42486-020-00027-1","article-title":"Device free human activity and fall recognition using WiFi channel state information (CSI)","volume":"2","author":"Damodaran","year":"2020","journal-title":"CCF Trans. Pervasive Comput. Interact."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jia, R., and Liu, B. (2013, January 5\u20138). Human daily activity recognition by fusing accelerometer and multi-lead ECG data. Proceedings of the 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013), KunMing, China.","DOI":"10.1109\/ICSPCC.2013.6664056"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Castro, D., Coral, W., Rodriguez, C., Cabra, J., and Colorado, J. (2017). Wearable-Based Human Activity Recognition Using an IoT Approach. J. Sens. Actuator Netw., 6.","DOI":"10.3390\/jsan6040028"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"R155","DOI":"10.1088\/0967-3334\/26\/5\/R01","article-title":"Wavelet transforms and the ECG: A review","volume":"26","author":"Addison","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kher, D.R., Pawar, T., and Thakar, D. (2015). Impact Analysis of Body Movements on Wearable Ambulatory Electrocardiogram, OMICS International.","DOI":"10.4172\/978-1-63278-048-5-049"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting Falls with Wearable Sensors Using Machine Learning Technique","volume":"14","author":"Turan","year":"2014","journal-title":"Sensors"},{"key":"ref_22","unstructured":"Md Shahiduzzaman (2015). Fall detection by Acceleromeer and Heart rate variability Measurement. Glob. J. Comput. Sci. Technol. G Interdiscip., 15, Available online: https:\/\/computerresearch.org\/index.php\/computer\/article\/view\/1328."},{"key":"ref_23","unstructured":"MathWorks (2018, August 20). Classify Time Series Using Wavelet Analysis and Deep Learning. Available online: https:\/\/de.mathworks.com\/help\/wavelet\/examples\/signal-classification-with-wavelet-analysis-and-convolutional-neural-networks.html."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hoang, H.V., and Tran, M. (2017, January 15\u201318). DeepSense-Inception: Gait Identification from Inertial Sensors with Inception-like Architecture and Recurrent Network. Proceedings of the 2017 13th International Conference on Computational Intelligence and Security (CIS), Hong Kong, China.","DOI":"10.1109\/CIS.2017.00138"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Melillo, P., Castaldo, R., Sannino, G., Orrico, A., de Pietro, G., and Pecchia, L. (2015, January 25\u201329). Wearable technology and ECG processing for fall risk assessment, prevention and detection. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320186"},{"key":"ref_26","unstructured":"Butt, F.S. (2019). Fall Detection Using Machine Learning Techniques on ECG Signals. [Master\u2019s Thesis, Frankfurt University of Applied Sciences]."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, Y.G., Shin, D., Park, M.Y., Lee, S., Jeon, M.S., Yoon, D., and Park, R.W. (2017). ECG-ViEW II, a freely accessible electrocardiogram database. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176222"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"La Blunda, L., Corral-Plaza, D., Wagner, M., Ortiz, G., and Medina-Bulo, I. (2019, January 7\u20139). Distributed real-time based human activity analysis system. Proceedings of the 16th International Conference on Applied Computing in Cagliari (ITALY), Cagliari, Italy.","DOI":"10.33965\/ac2019_201912C027"},{"key":"ref_30","unstructured":"La Blunda, L. (2017, January 2). Fall event Analysis based on sensor fusion. Proceedings of the Las Jornadas Predoctorales de la Escuela Superior de Ingenier\u00eda (JORPRESI), C\u00e1diz, Spain."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"193060","DOI":"10.1109\/ACCESS.2020.3032497","article-title":"A Wearable Fall Detection System Based on Body Area Networks","volume":"8","author":"Wagner","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"World Medical Association (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310, 2191\u20132194.","DOI":"10.1001\/jama.2013.281053"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1136\/hrt.2004.057307","article-title":"The normal ECG in childhood and adolescence","volume":"91","author":"Dickinson","year":"2005","journal-title":"Heart"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J., and Vargas-Bonilla, J.F. (2017). SisFall: A Fall and Movement Dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fleming, J., and Brayne, C. (2008). Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90. BMJ, 337.","DOI":"10.1136\/bmj.a2227"},{"key":"ref_36","first-page":"3","article-title":"Detecting Human Falls with a 3-Axis Digital Accelerometer","volume":"43","author":"Jing","year":"2009","journal-title":"Analog Dialogue"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting complex activity datasets in highly rich networked sensor environments. Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_39","unstructured":"Reiss, A., and Stricker, D. (2021, February 02). Physical Activity Monitoring Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/PAMAP2+Physical+Activity+Monitoring."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Tr\u00f6ster, G. (2008). Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection. European Conference on Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-540-77690-1_2"},{"key":"ref_41","unstructured":"Guan, Y., and Pl\u00f6tz, T. Ensembles of Deep LSTM Learners for Activity Recognition Using Wearables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies."},{"key":"ref_42","unstructured":"(2018, December 02). Analog to Digital Conversion. Available online: https:\/\/learn.sparkfun.com\/tutorials\/analog-to-digital-conversion\/all."},{"key":"ref_43","first-page":"1114","article-title":"Noise Removal for Baseline wander and power line in Electrocardiograph Signals","volume":"4","author":"Manivel","year":"2015","journal-title":"Int. J. Adv. Res. Electr. Electron. Instrum. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Das, N., and Chakraborty, M. (2017, January 3\u20135). Performance Analysis of FIR and IIR filters for ECG Signal De-noising based on SNR. Proceedings of the 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India.","DOI":"10.1109\/ICRCICN.2017.8234487"},{"key":"ref_45","unstructured":"Zhang, D. (2005, January 1\u20134). Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China."},{"key":"ref_46","first-page":"15","article-title":"Baseline drift removal and de-noising of the ECG signal using Wavelet Transform","volume":"95","author":"Ara","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9295029","DOI":"10.1155\/2017\/9295029","article-title":"Comparison of Baseline Wander Removal Techniques considering the preservation of ST changes in the Ischemic ECG: A simulation Study","volume":"2017","author":"Lenis","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_48","unstructured":"Sanjit, K.M. (1998). Digital Signal Processing, McGraw-Hill."},{"key":"ref_49","unstructured":"Milchevski, A., and Guse, M. (2016). Performance Evaluation of FIR and IIR Filtering of ECG Signals. International Conference on ICT Innovations, Springer."},{"key":"ref_50","first-page":"1105","article-title":"Comparative study of FIR and IIR filters for the removal of Baseline noises from ECG signal","volume":"2","author":"Rani","year":"2011","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_51","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). How transferable are features in deep neural networks?. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."},{"key":"ref_52","unstructured":"(2018, December 07). ImageNet. Available online: http:\/\/www.image-net.org\/."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_54","unstructured":"(2018, December 10). Visualize Activations of a Convolutional Neural Network. Available online: https:\/\/de.mathworks.com\/help\/deeplearning\/examples\/visualize-activations-of-a-convolutional-neural-network.html."},{"key":"ref_55","unstructured":"Kher, R. (2020). Wearable Ambulatory Electrocardiogram (ECG) and EEG Dataset. IEEE Dataport."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_57","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Iwana, B.K., and Uchida, S. (2020). An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. arXiv.","DOI":"10.1371\/journal.pone.0254841"},{"key":"ref_59","unstructured":"Cui, Z., Chen, W., and Chen, Y. (2016). Multi-Scale Convolutional Neural Networks for Time Series Classification. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_63","unstructured":"Geoffrey Hinton, N.S., and Swersky, K. (2021, February 02). Neural Networks for Machine Learning Online Course. Available online: https:\/\/www.classcentral.com\/course\/neuralnets-398."},{"key":"ref_64","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/63\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:26Z","timestamp":1760159966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,3]]},"references-count":64,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["info12020063"],"URL":"https:\/\/doi.org\/10.3390\/info12020063","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,3]]}}}