{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:10Z","timestamp":1760188570379,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T00:00:00Z","timestamp":1557705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Sensor technology provides the real-time monitoring of data in several scenarios that contribute to the improved security of life and property. Crowd condition monitoring is an area that has benefited from this. The basic context-aware framework (BCF) uses activity recognition based on emerging intelligent technology and is among the best that has been proposed for this purpose. However, accuracy is low, and the false negative rate (FNR) remains high. Thus, the need for an enhanced framework that offers reduced FNR and higher accuracy becomes necessary. This article reports our work on the development of an enhanced context-aware framework (EHCAF) using smartphone participatory sensing for crowd monitoring, dimensionality reduction of statistical-based time-frequency domain (SBTFD) features, and enhanced individual behavior estimation (IBEenhcaf). The experimental results achieved 99.1% accuracy and an FNR of 2.8%, showing a clear improvement over the 92.0% accuracy, and an FNR of 31.3% of the BCF.<\/jats:p>","DOI":"10.3390\/e21050487","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T11:00:57Z","timestamp":1557745257000},"page":"487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction"],"prefix":"10.3390","volume":"21","author":[{"given":"Fatai Idowu","family":"Sadiq","sequence":"first","affiliation":[{"name":"Faculty of Engineering, School of Computing, UTM &amp; Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia"},{"name":"Faculty of Physical Sciences, Ambrose Alli University, P.M.B 14, 310101 Ekpoma, Edo State, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9746-8459","authenticated-orcid":false,"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, UTM &amp; Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia"},{"name":"Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic"},{"name":"Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roliana","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, UTM &amp; Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5992-2574","authenticated-orcid":false,"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[{"name":"Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Duives, D.C., Wang, G., and Kim, J. (2019). Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data. Sensors, 19.","DOI":"10.3390\/s19020382"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, F., Al-Qaness, M., Zhang, Y., Zhao, B., and Luan, X. (2016). A robust and device-free system for the recognition and classification of elderly activities. Sensors, 16.","DOI":"10.3390\/s16122043"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3923\/itj.2013.1199.1205","article-title":"Crowd abnormal behavior detection based on machine learning","volume":"12","author":"Zhang","year":"2013","journal-title":"Inf. Technol. J."},{"key":"ref_4","unstructured":"Sadiq, F.I., Selamat, A., Ibrahim, R., and Ondrej, K. (2018, September 01). Improved feature extraction method with statistical based time frequency domain for classification oindividual activity recognition in a crowd scenario. Available online: www.news.unimas.my\/conference\/eventdetail\/4309\/-\/-."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2017.11.001","article-title":"Adaptive monitoring system for e-health smart homes","volume":"43","author":"Mshali","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.adhoc.2013.02.006","article-title":"Context aware ad hoc network for mitigation of crowd disasters","volume":"18","author":"Ramesh","year":"2014","journal-title":"Ad Hoc Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13174-015-0040-6","article-title":"Smart crowds in smart cities: Real life, city scale deployments of a smartphone based participatory crowd management platform","volume":"6","author":"Franke","year":"2015","journal-title":"JISA"},{"key":"ref_8","unstructured":"Sadiq, F.I., Selamat, A., Ondrej, K., and Ibrahim, R. Impacts of feature selection on classification of individual activity recognitions for prediction of crowd disasters. Int. J. Intell. Inf. Database Syst., in press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.ssci.2013.03.007","article-title":"Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems","volume":"57","author":"Yaseen","year":"2013","journal-title":"Saf. Sci."},{"key":"ref_10","unstructured":"Ngo, M.Q., Haghighi, P.D., and Burstein, F. (2019, May 08). A crowd monitoring framework using emotion analysis of social media for emergency management in mass gatherings. Available online: https:\/\/arxiv.org\/abs\/1606.00751."},{"key":"ref_11","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_12","unstructured":"Pressac (2019, April 12). Benefits of smart Sensor Technology. Available online: https:\/\/www.pressac.com\/insights\/benefits-of-smart-sensor-technology\/."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jnca.2016.03.013","article-title":"User context recognition using smartphone sensors and classification models","volume":"66","author":"Otebolaku","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_14","unstructured":"Holgersson, P., and \u00c5kerberg, F. Analysis of Activity Recognition and the Influence of Feature Extraction and Selection in an Android Based Device. Master\u2019s Theses, Lund University, Lund, Sweden, 2015."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"503291","DOI":"10.1155\/2014\/503291","article-title":"Activity recognition on smartphones via sensor-fusion and KDA-based SVMs","volume":"10","author":"Khan","year":"2014","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1016\/j.eswa.2014.04.037","article-title":"Unsupervised learning for human activity recognition using smartphone sensors","volume":"41","author":"Kwon","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_18","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. Ubiquit. Comput."},{"key":"ref_19","unstructured":"Reiss, A., Hendeby, G., and Stricker, D. (2013, January 24\u201326). A Competitive approach for human activity recognition on smartphones. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Bruges, Belgium."},{"key":"ref_20","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN. 201, Bruges, Belgium."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kumari, S., and Mitra, S.K. (2011, January 15\u201317). Human action recognition using DFT. Proceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Hubli, India.","DOI":"10.1109\/NCVPRIPG.2011.58"},{"key":"ref_22","unstructured":"Rahman, M. (2011). Applications of Fourier Transforms to Generalized Functions, WIT Press."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Phan, T. (2014, January 13\u201317). Improving activity recognition via automatic decision tree pruning. Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2641310"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cao, L., Wang, Y., Zhang, B., Jin, Q., and Vasilakos, A.V. (2017). GCHAR: An efficient group-based context\u2013aware human activity recognition on smartphone. J. Parallel Distr. Comput., in press.","DOI":"10.1016\/j.jpdc.2017.05.007"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhenyu, H., and Lianwen, J. (2009, January 11\u201314). Activity Recognition from acceleration data Based on Discrete Consine Transform and SVM. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5346042"},{"key":"ref_26","unstructured":"Roggen, D., Wirz, M., Tr\u00f6ster, G., and Helbing, D. (2019, May 08). Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods. Available online: https:\/\/arxiv.org\/abs\/1109.1664."},{"key":"ref_27","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 MitigatingMobile Sensing Heterogeneities for Activity Recognition. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Korea.","DOI":"10.1145\/2809695.2809718"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Sukor, A.A., Zakaria, A., and Rahim, N.A. (2018, January 9\u201310). Activity recognition using accelerometer sensor and machine learning classifiers. Proceedings of the 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Batu Feringghi, Malaysia.","DOI":"10.1109\/CSPA.2018.8368718"},{"key":"ref_30","unstructured":"Haritha, V. (2019, May 08). Physical Human Activity Recognition Using Machine Learning Algorithms. Available online: https:\/\/arrow.dit.ie\/cgi\/viewcontent.cgi?article=1117&context=scschcomdis."},{"key":"ref_31","first-page":"1","article-title":"An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction","volume":"2016","author":"Zainuddin","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.eswa.2014.07.052","article-title":"Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition","volume":"42","author":"Chernbumroong","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.sigpro.2017.06.001","article-title":"One-shot learning based pattern transition map for action early recognition","volume":"143","author":"Ji","year":"2018","journal-title":"Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yasser, E.-M., Hsieh, T.-Y., Shivakumar, M., Kim, D., and Honavar, V. (2018). Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Med. Genom., 11.","DOI":"10.1186\/s12920-018-0388-0"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1016\/j.egypro.2012.01.211","article-title":"Context-aware Mobile platform for intellectual disaster alerts system","volume":"16","author":"Chang","year":"2012","journal-title":"Energy Procedia"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ravindran, R., Suchdev, R., Tanna, Y., and Swamy, S. (2014, January 1\u20132). Context aware and pattern oriented machine learning framework (CAPOMF) for Android. Proceedings of the Advances in Engineering and Technology Research (ICAETR), Unnao, India.","DOI":"10.1109\/ICAETR.2014.7012912"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1504\/IJAHUC.2007.014070","article-title":"A survey on context-aware systems","volume":"2","author":"Baldauf","year":"2007","journal-title":"Int. J. Ad Hoc Ubiquit. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Otebolaku, A., and Lee, G.M. (2018). A framework for exploiting internet of things for context-aware trust-based personalized services. Mob. Inf. Syst., 2018.","DOI":"10.1155\/2018\/6138418"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bouguessa, A., Mebarki, L.A., and Boudaa, B. (2015, January 28\u201330). Context-aware adaptation for sustaining disaster management. Proceedings of the 12th International Symposium on Programming and Systems (ISPS), Algiers, Algeria.","DOI":"10.1109\/ISPS.2015.7244980"},{"key":"ref_40","unstructured":"Markets, A.M. (2019, May 08). Available online: https:\/\/www.marketsandmarkets.com\/PressReleases\/context-aware-computing.asp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1093\/comjnl\/bxy065","article-title":"A policy model and framework for context-aware access control to information resources","volume":"62","author":"Kayes","year":"2019","journal-title":"Comput. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"116","DOI":"10.15837\/ijccc.2017.1.2787","article-title":"Feature analysis to human activity recognition","volume":"12","author":"Suto","year":"2017","journal-title":"Int. J. Comput. Commun. Contr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.autcon.2016.08.015","article-title":"Smartphone-based construction workers\u2019 activity recognition and classification","volume":"71","author":"Akhavian","year":"2016","journal-title":"Automat. Constr."},{"key":"ref_44","first-page":"385","article-title":"Stampede prediction based on individual activity recognition for context-aware framework using sensor-fusion in a crowd scenarios","volume":"297","author":"Sadiq","year":"2017","journal-title":"SoMeT"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.chemgeo.2013.01.010","article-title":"Multi-sample comparison of detrital age distributions","volume":"341","author":"Vermeesch","year":"2013","journal-title":"Chem. Geol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.eswa.2012.07.021","article-title":"A comparative study of efficient initialization methods for the k-means clustering algorithm","volume":"40","author":"Celebi","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02289565","article-title":"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis","volume":"29","author":"Kruskal","year":"1964","journal-title":"Psychometrika"},{"key":"ref_48","unstructured":"Fruin, J.J. (1993). The causes and prevention of crowd disasters. Eng. Crowd Saf., 99\u2013108."},{"key":"ref_49","unstructured":"Helbing, D., Johansson, A., and Al-Abideen, H.Z. (2019, May 08). Crowd turbulence: The physics of crowd disasters. Available online: https:\/\/arxiv.org\/abs\/0708.3339."},{"key":"ref_50","unstructured":"Rodrigues Leal Moitinho de Almeida, M. (2019, May 08). Human stampedes: A scoping review. Available online: http:\/\/digibuo.uniovi.es\/dspace\/bitstream\/10651\/39115\/6\/TFM_MariaRodriguesLMdeAlmeida.pdf."},{"key":"ref_51","first-page":"34","article-title":"Human activity recognition based on the hierarchical feature selection and classification framework","volume":"2015","author":"Zheng","year":"2015","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.procs.2016.09.070","article-title":"Integrating features for accelerometer-based activity recognition","volume":"98","author":"Atasoy","year":"2016","journal-title":"Procedia Comput. Sci."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/5\/487\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:29Z","timestamp":1760187089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/5\/487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,13]]},"references-count":52,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["e21050487"],"URL":"https:\/\/doi.org\/10.3390\/e21050487","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2019,5,13]]}}}