{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T09:56:37Z","timestamp":1769334997311,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T00:00:00Z","timestamp":1528761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents our approach that uses feature selection to refine the clustering of accelerometer data to detect physical activity. This also has a positive effect on the computational burden that is associated with processing large sets of data, as energy efficiency and resource use is decreased because less data is processed by the clustering algorithms. Raw accelerometer data, obtained from smartphones and smartwatches, have been preprocessed to extract both time and frequency domain features. Principle component analysis feature selection (PCAFS) and correlation feature selection (CFS) have been used to remove redundant features. The reduced feature sets have then been evaluated against three widely used clustering algorithms, including hierarchical clustering analysis (HCA), k-means, and density-based spatial clustering of applications with noise (DBSCAN). Using the reduced feature sets resulted in improved separability, reduced uncertainty, and improved efficiency compared with the baseline, which utilized all features. Overall, the CFS approach in conjunction with HCA produced higher Dunn Index results of 9.7001 for the phone and 5.1438 for the watch features, which is an improvement over the baseline. The results of this comparative study of feature selection and clustering, with the specific algorithms used, has not been performed previously and provides an optimistic and usable approach to recognize activities using either a smartphone or smartwatch.<\/jats:p>","DOI":"10.3390\/informatics5020029","type":"journal-article","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T10:58:32Z","timestamp":1528801112000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9420-2452","authenticated-orcid":false,"given":"Chelsea","family":"Dobbins","sequence":"first","affiliation":[{"name":"Department of Computer Science, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-1777","authenticated-orcid":false,"given":"Reza","family":"Rawassizadeh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Rochester, 3700 Wegmans Hall, P.O. 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J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/MIC.2010.4","article-title":"Computing for human experience: Semantics-empowered sensors, services, and social computing on the ubiquitous Web","volume":"14","author":"Sheth","year":"2010","journal-title":"IEEE Internet Comput."},{"key":"ref_4","unstructured":"Cisco (2015). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014\u20132019, Cisco."},{"key":"ref_5","unstructured":"Quantified Self Labs (2018, January 31). Quantified Self. Available online: http:\/\/quantifiedself.com."},{"key":"ref_6","unstructured":"Khan, S., and Marzec, E. (2014). Wearables: On-body computing devices are ready for business. Tech Trends 2014: Inspiring Disruption, deloitte, ed., Deloitte University Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.ipm.2014.07.008","article-title":"Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization","volume":"51","author":"Machado","year":"2015","journal-title":"Inf. Process. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MEMB.2010.936554","article-title":"Wearable Sensors and Systems. From Enabling Technology to Clinical Applications","volume":"29","author":"Bonato","year":"2010","journal-title":"IEEE Eng. Med. Biol. Soc. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/1743-0003-2-17","article-title":"Wearable Feedback Systems For Rehabilitation","volume":"2","author":"Sung","year":"2005","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jad.2013.01.014","article-title":"A review of lifestyle factors that contribute to important pathways associated with major depression: Diet, sleep and exercise","volume":"148","author":"Lopresti","year":"2013","journal-title":"J. Affect. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1109\/TKDE.2016.2592527","article-title":"Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data","volume":"28","author":"Rawassizadeh","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1007\/s00779-011-0403-3","article-title":"A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation","volume":"15","author":"Lee","year":"2011","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_13","unstructured":"Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology, Viking Penguin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/scientificamerican0805-32","article-title":"Kryder\u2019s Law","volume":"293","author":"Walter","year":"2005","journal-title":"Sci. Am."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MPRV.2018.011591063","article-title":"NoCloud: Exploring Network Disconnection through On-Device Data Analysis","volume":"17","author":"Rawassizadeh","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1136\/bjsports-2013-093154","article-title":"Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiological Studies: Issues and Challenges","volume":"48","author":"Lee","year":"2014","journal-title":"Br. J. Sports Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, Y.-S., and Cho, S.-B. (2012). Recognizing multi-modal sensor signals using evolutionary learning of dynamic Bayesian networks. Pattern Anal. Appl.","DOI":"10.1007\/s10044-012-0300-z"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Doherty, A.R., Gurrin, C., and Smeaton, A.F. (2011, January 13\u201315). Mining User Activity as a Context Source for Search and Retrieval. Proceedings of the 2011 International Conference on Semantic Technology and Information Retrieval (STAIR), Tempe, AZ, USA.","DOI":"10.1109\/STAIR.2011.5995782"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Phan, T. (2012, January 6). Generating Natural-Language Narratives from Activity Recognition with Spurious Classification Pruning. Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones\u2014PhoneSense\u201912, Toronto, ON, Canada.","DOI":"10.1145\/2389148.2389161"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10701","DOI":"10.1007\/s11042-015-3188-y","article-title":"Towards unsupervised physical activity recognition using smartphone accelerometers","volume":"76","author":"Lu","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","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 (SenSys\u201915), Seoul, Korea.","DOI":"10.1145\/2809695.2809718"},{"key":"ref_22","first-page":"39","article-title":"Advances in Intelligent Systems and Computing","volume":"Volume 368","author":"Herrero","year":"2015","journal-title":"10th International Conference on Soft Computing Models in Industrial and Environmental Applications"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1145\/2629633","article-title":"Wearables: Has the Age of Smartwatches Finally Arrived?","volume":"58","author":"Rawassizadeh","year":"2014","journal-title":"Commun. ACM"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Uddin, M., Salem, A., Nam, I., and Nadeem, T. (2015, January 15). Wearable Sensing Framework for Human Activity Monitoring. Proceedings of the 2015 Workshop on Wearable Systems and Applications, Florence, Italy.","DOI":"10.1145\/2753509.2753513"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saeedi, R., Purath, J., Venkatasubramanian, K., and Ghasemzadeh, H. (2014, January 26\u201330). Toward Seamless Wearable Sensing: Automatic On-Body Sensor Localization for Physical Activity Monitoring. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944843"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.bbe.2017.04.004","article-title":"Physical activity recognition by smartphones, a survey","volume":"37","author":"Morales","year":"2017","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_27","unstructured":"Fortino, G., Gravina, R., Guerrieri, A., and Di Fatta, G. (October, January 30). Engineering Large-Scale Body Area Networks Applications. Proceedings of the 8th International Conference on Body Area Networks, Boston, MA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A Survey on Human Activity Recognition using Wearable Sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dias, R., and Machado da Silva, J. (2014, January 16\u201319). A Flexible Wearable Sensor Network for Bio-Signals and Human Activity Monitoring. Proceedings of the 2014 11th International Conference on Wearable and Implantable Body Sensor Networks Workshops, Zurich, Switzerland.","DOI":"10.1109\/BSN.Workshops.2014.20"},{"key":"ref_30","first-page":"475","article-title":"Measuring Physical Activity with Sensors: A Qualitative Study","volume":"150","author":"Dias","year":"2009","journal-title":"Med. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1016\/j.jbiomech.2006.10.003","article-title":"Kinematic quantitation of the patellar tendon reflex using a tri-axial accelerometer","volume":"40","author":"Mamizuka","year":"2007","journal-title":"J. Biomech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/S0021-9290(01)00231-7","article-title":"Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems","volume":"35","author":"Mayagoitia","year":"2002","journal-title":"J. Biomech."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.medengphy.2004.11.006","article-title":"A description of an accelerometer-based mobility monitoring technique","volume":"27","author":"Lyons","year":"2005","journal-title":"Med. Eng. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sapir, I., Markovic, S., Wagenaar, R., and Little, T. (2011, January 23\u201326). Continuous Functional Activity Monitoring Based on Wearable Tri-axial Accelerometer and Gyroscope. Proceedings of the 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, Ireland.","DOI":"10.4108\/icst.pervasivehealth.2011.245966"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 6\u20138). Creating and Benchmarking a New Dataset for Physical Activity Monitoring. Proceedings of the 5th Workshop on Affect and Behaviour Related Assistance (ABRA), Crete, Greece.","DOI":"10.1145\/2413097.2413148"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-540-24646-6_1","article-title":"Activity Recognition from User-Annotated Acceleration Data","volume":"3001","author":"Bao","year":"2004","journal-title":"Pervasive Comput."},{"key":"ref_37","unstructured":"Song, W., Ade, C., Broxterman, R., Barstow, T., Nelson, T., and Warren, S. (September, January 28). Activity Recognition in Planetary Navigation Field Tests Using Classification Algorithms Applied to Accelerometer Data. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA."},{"key":"ref_38","unstructured":"Ravi, N., Dandekar, N., Mysore, P., and Littman, M.L. (2005, January 9\u201313). Activity Recognition from Accelerometer Data. Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence, Pittsburgh, PA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Krishnan, N.C., and Panchanathan, S. (April, January 31). Analysis of low resolution accelerometer data for continuous human activity recognition. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA.","DOI":"10.1109\/ICASSP.2008.4518365"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1249\/MSS.0b013e3181a24536","article-title":"Detection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer","volume":"41","author":"Bonomi","year":"2009","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_42","unstructured":"Srivastava, P., and Wong, W.-C. (2012, January 23\u201328). Hierarchical Human Activity Recognition Using GMM. Proceedings of the AMBIENT 2012: The Second International Conference on Ambient Computing, Applications, Services and Technologies, Barcelona, Spain."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mokaya, F., Nguyen, B., Kuo, C., Jacobson, Q., Rowe, A., and Zhang, P. (2013, January 8\u201311). MARS: A Muscle Activity Recognition System Enabling Self-configuring Musculoskeletal Sensor Networks. Proceedings of the 12th ACM\/IEEE Conference on Information Processing in Sensor Networks (IPSN), Philadelphia, PA, USA.","DOI":"10.1145\/2461381.2461406"},{"key":"ref_44","unstructured":"Long, X., Yin, B., and Aarts, R.M. (2009, January 3\u20136). Single-Accelerometer-Based Daily Physical Activity Classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Abdullah, S., Lane, N.D., and Choudhury, T. (2012, January 22\u201326). Towards Population Scale Activity Recognition: A Framework for Handling Data Diversity. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, ON, Canada.","DOI":"10.1609\/aaai.v26i1.8323"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing Techniques for Context Recognition from Accelerometer Data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_47","unstructured":"MathWorks (2014, September 03). Practical Introduction to Frequency-Domain Analysis. Available online: http:\/\/www.mathworks.co.uk\/help\/signal\/examples\/practical-introduction-to-frequency-domain-analysis.html."},{"key":"ref_48","first-page":"1254","article-title":"Predicting term and preterm delivery with transabdominal uterine electromyography","volume":"101","author":"Maner","year":"2003","journal-title":"Obstet. Gynecol."},{"key":"ref_49","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","unstructured":"Bins, J., and Draper, B.A.B. (2001, January 7\u201314). Feature selection from huge feature sets. Proceedings of the Eighth IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_51","first-page":"3","article-title":"The Add-on Impact of Mobile Applications in Learning Strategies: A Review Study","volume":"13","author":"Jeng","year":"2010","journal-title":"J. Educ. Technol. Soc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_53","first-page":"856","article-title":"Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution","volume":"3","author":"Yu","year":"2003","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Haindl, M., Somol, P., Ververidis, D., and Kotropoulos, C. (2006). Feature Selection Based on Mutual Correlation. Iberoamerican Congress on Pattern Recognition, Springer.","DOI":"10.1007\/11892755_59"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data Clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_56","unstructured":"Wagstaff, K., Cardie, C., Rogers, S., and Schroedl, S. (July, January 28). Constrained K-means Clustering with Background Knowledge. Proceedings of the Eighteenth International Conference on Machine Learning (ICML), Williamstown, MA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2007","journal-title":"Knowl. Inf. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rti.2004.05.007","article-title":"Identification of tuberculosis bacteria based on shape and color","volume":"10","author":"Forero","year":"2004","journal-title":"Real-Time Imaging"},{"key":"ref_59","first-page":"5","article-title":"Structuring and Presenting Lifelogs based on Location Data","volume":"4","author":"Kikhia","year":"2011","journal-title":"Image (IN)"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TPAMI.2003.1240115","article-title":"Bagging for path-based clustering","volume":"25","author":"Fischer","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2005). Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/b107408"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-09823-4"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of Clustering Algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., and Wu, J. (2010, January 13\u201317). Understanding of Internal Clustering Validation Measures. Proceedings of the IEEE Internatinal Conference on Data Mining, Sydney, NSW, Australia.","DOI":"10.1109\/ICDM.2010.35"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_67","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2015). An Introduction to Statistical Learning with Applications in R, Springer."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Senliol, B., Gulgezen, G., Yu, L., and Cataltepe, Z. (2008, January 27\u201329). Fast Correlation Based Filter (FCBF) with a different search strategy. Proceedings of the 23rd International Symposium on Computer and Information Sciences (ISCIS\u201908), Istanbul, Turkey.","DOI":"10.1109\/ISCIS.2008.4717949"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"WikiChip (2018, April 13). A11 Bionic\u2014Apple. Available online: https:\/\/en.wikichip.org\/wiki\/apple\/ax\/a11.","DOI":"10.3199\/iscb.13.A11"},{"key":"ref_70","unstructured":"(2018, April 13). Nvidia Jetson. Available online: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems-dev-kits-modules\/."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2011, January 7\u20138). A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors. Proceedings of the 6th International Conference on Body Area Networks (BodyNets\u201911), Beijing, China.","DOI":"10.4108\/icst.bodynets.2011.247018"},{"key":"ref_72","unstructured":"(2018, January 31). NHS Exercise. Available online: https:\/\/www.nhs.uk\/live-well\/exercise\/."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.neucom.2016.02.088","article-title":"Detecting Physical Activity within Lifelogs towards Preventing Obesity and Aiding Ambient Assisted Living","volume":"230","author":"Dobbins","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:21Z","timestamp":1760195301000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,12]]},"references-count":73,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["informatics5020029"],"URL":"https:\/\/doi.org\/10.3390\/informatics5020029","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,12]]}}}