{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:48:30Z","timestamp":1777704510097,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,7,26]],"date-time":"2018-07-26T00:00:00Z","timestamp":1532563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,11,20]]},"abstract":"<jats:p>User activity classification is one of the most popular research topic in the domain of health care and social care, since this automated technology can provide monitoring and understanding of activities of patients. Smartphone inbuilt sensors based User Activity Classifier (UAC) recognizes user activities using features extracted from sensors like accelerometer and gyroscope in build in smartphones. In this research paper, we are proposing a new user activity classifier system using Layer Recurrent Neural Network (LRNN) which is Artificial Neural Network (ANN). We utilize synthesized data, containing features of user activity classification system, extracted from the raw data recorded in smartphones. With these derived features, we train and test Layer Recurrent Neural Network classifier for user activity classifier. In order to evaluate this system, we have compared the performance of this Layer Recurrent Neural Network based user activity classifier against the convention Multilayer Perceptron (MLP) and Naive Bayes based user activity classifier. Test results show that the proposed Layer Recurrent Neural Network -based user activity classifier is able to recognize user activities reliably and outperforms the Multilayer Perceptron based user activity classifier. We have achieved the classification accuracy of 98.56% for the activities. The results are much more accurate than Multilayer Perceptron based classifier and Naive Bayes classifier.<\/jats:p>","DOI":"10.3233\/jifs-169793","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T19:28:17Z","timestamp":1532719697000},"page":"5085-5097","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Layer recurrent neural network based intelligent user activity classification model using smartphone"],"prefix":"10.1177","volume":"35","author":[{"given":"Harshit","family":"Jain","sequence":"first","affiliation":[{"name":"Segmentation and Targeting, Commercial Analytics, IQVIA, USA"}]},{"given":"Nuzhat","family":"Fatema","sequence":"additional","affiliation":[{"name":"International Institute of Health Management and Research, New Delhi, India"}]}],"member":"179","published-online":{"date-parts":[[2018,7,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"SuX. TongH. and JiP. Activity Classification with Smartphone Sensors it Tsinghua science and technology issn 1007-0214 02\/11 19(3) (2014) 235\u2013249.","DOI":"10.1109\/TST.2014.6838194"},{"key":"e_1_3_1_3_2","volume-title":"IEEE Symposium on Computers & Informatics","author":"Ayu M.A.","year":"2011","unstructured":"AyuM.A., MantoroT., MatinA.F.A. and BasamhS.S.O., Intelligent Environment Research Group (INTEG). Recognizing User Activity Based on Accelerometer Data from a Mobile Phone, IEEE Symposium on Computers & Informatics, 2011."},{"key":"e_1_3_1_4_2","first-page":"5","volume-title":"CENTERIS \/ ProjMAN \/ HCist","author":"Parka S.U.","year":"2016","unstructured":"ParkaS.U., ParkaJ.H., Al-masniaM.A., Al-antariaM.A., UddinbZ.Md. and KimaT.S., A Depth Camera-based User Activity Classification via Deep Learning Re-current Neural Network for Health and Social Care Ser-vices, Conference on ENTERprise Information Systems \/ International Conference on Project MANagement \/ Con-ference on Health and Social Care Information Systems and Technologies, CENTERIS \/ ProjMAN \/ HCist (2016), 5\u20137."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CADS.2017.8310680"},{"key":"e_1_3_1_6_2","first-page":"3963","volume-title":"IEEE Conferences","author":"K\u00e4se N.","year":"2017","unstructured":"K\u00e4seN. and BabaeeM., Gerhard Rigoll Multi-view human activity recognition using motion frequency, IEEE Conferences (2017), 3963\u20133967."},{"key":"e_1_3_1_7_2","first-page":"585","volume-title":"Procedia Computer Science","volume":"10","author":"Paniagua C.","year":"2012","unstructured":"PaniaguaC., FloresH., SriramaS.N., Mobile Sensor Data Classification for Human Activity Recognition using MapReduce on Cloud, The 9th Inter-national Conference on Mobile Web Information Sys-tems, Procedia Computer Science10 (2012), 585\u2013592."},{"key":"e_1_3_1_8_2","first-page":"1","volume-title":"IEEE Conferences","author":"Liu R.","year":"2010","unstructured":"LiuR. and LiuM., Recognizing Human Activi-ties Based on Multi-Sensors Fusion, IEEE Conferences (2010), 1\u20134."},{"key":"e_1_3_1_9_2","unstructured":"Reyes-OrtizJ.L. AnguitaD. OnetoL. and ParraX. Smartphone-Based Classification of User Activities and Postural Transitions Data Set https:\/\/archive.ics.uci.edu\/ml\/datasets\/Smartphone-Based+Classification+of+User+Activities+and+Postural+Transitions."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/jsan6040028"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169793","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169793","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:22Z","timestamp":1777455682000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,26]]},"references-count":10,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,11,20]]}},"alternative-id":["10.3233\/JIFS-169793"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169793","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,26]]}}}