{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:34Z","timestamp":1777703374869,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,6,10]],"date-time":"2019-06-10T00:00:00Z","timestamp":1560124800000},"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":[[2019,7,9]]},"abstract":"<jats:p>Managing and Mining mobile sensor data has become a topic of advanced research in several fields of computer science, such as the distributed systems, the database systems, and data mining. The main objective of the sensor based applications is to make the real-time decision which has been proved to be very challenging due to the high resource-constrained computing and the enormous volume of sensor data generated by Wireless Sensor Networks (WSNs). This challenge motivates the sensor research community to explore new data mining techniques to extract information from large continuous raw data streams obtained from WSNs. Existing traditional data mining methods are not directly suited to WSNs due to the aggressive nature of sensor data and the presence of anomalies or outliers in WSNs. This work provides an overview of how traditional outlier detection method algorithms are revised and implemented in the application of Human Activity Recognition (HAR). Based on the limitations of the existing technique, a hybrid outlier detection method is proposed.<\/jats:p>","DOI":"10.3233\/jifs-181315","type":"journal-article","created":{"date-parts":[[2019,6,11]],"date-time":"2019-06-11T11:55:09Z","timestamp":1560254109000},"page":"771-782","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving data classification accuracy in sensor networks using hybrid outlier detection in HAR"],"prefix":"10.1177","volume":"37","author":[{"given":"Nivetha","family":"Gopalakrishnan","sequence":"first","affiliation":[{"name":"University College of Engineering Panruti, Panruti, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Venkatalakshmi","family":"Krishnan","sequence":"additional","affiliation":[{"name":"University College of Engineering Tindivanam, Tindivanam, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,6,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"M.R.Alam M.B.I.Reaz M.A.M.Ali S.A.Samad F.H.Hashim and M.K.Hamzah Human Activity Classification for Smart Home: A Multiagent Approach in procedia of IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010) Penang Malaysia 2010 pp. 511\u2013514.","DOI":"10.1109\/ISIEA.2010.5679411"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2830544.2830549"},{"key":"e_1_3_1_4_2","unstructured":"A.S.Evani B.Sreenivasan J.S.Sudesh M.Prakash and J.Bapat Activity recognition using wearable sensors for healthcare in the proceedings of Sensor Technologies and Applications (SENSORCOMM) (2013) 173\u2013177."},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Alsheikh et al. Machine Learning in Wireless Sensor Networks: Algorithms Strategies and Applications IEEE Communications - Surveys & Tutorials (2014) 1996\u20132018.","DOI":"10.1109\/COMST.2014.2320099"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2287504"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.11.032"},{"key":"e_1_3_1_8_2","unstructured":"M.Al-Zoubi A.Al-Dahoud and A.A.Yahya New outlier detection method based on fuzzy clustering WSEAS Transactions on Information Science and Applications (2010) 681\u2013690."},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"A.Bayat M.Pomplun and D.A.Tran A Study on Human Activity Recognition Using Accelerometer Data from Smartphones in Procedia Computer Science (2014) 450\u2013457.","DOI":"10.1016\/j.procs.2014.07.009"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"K.Bhaduri and M.Stolpe Distributed Data Mining in Sensor Networks. In: Aggarwal C. Managing and Mining Sensor Data.- Springer Boston (2013) 211\u2013236.","DOI":"10.1007\/978-1-4614-6309-2_8"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Cateni V.Colla M.Vannucci and M.Vannocci A Procedure for Building Reduced reliable Training Datasets from Real-WorldData International Conference on Artificial Intelligence and Applications AIA (IASTED 2014) Austria (2014) 17\u201319.","DOI":"10.2316\/P.2014.816-010"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2012-0607"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"J.Cheng X.Chen and M.Shen A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals (2013) 38\u201345.","DOI":"10.1109\/TITB.2012.2226905"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/653232"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"D.Arifoglu and A.Bouchachia Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks The 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017) (2017) 86\u201393.","DOI":"10.1016\/j.procs.2017.06.121"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2015.72"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.01.008"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"L.Duan L.Xu Y.Liu and J.Lee Cluster-based outlier detection Annals of Operating Research (2009) 151\u2013168.","DOI":"10.1007\/s10479-008-0371-9"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"A.Fawzy and H.M.Mokhtar O. Outliers detection and classification in wireless sensor networks Egyptian Informatics (2013) 157\u2013164.","DOI":"10.1016\/j.eij.2013.06.001"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2014.02.012"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"J.Gomes S.Krishnaswamy M.Gaber P.Sousa and M.Emars A Personalized Mobile Activity Recognition System in Proceedings of the IEEE International Conference on Mobile Data Management (MDM) Bengaluru Karnataka (2012) 316\u2013319.","DOI":"10.1109\/MDM.2012.33"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"S.A.Haque M.Rahman and S.M.Aziz Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare Sensors (2015) 8764\u20138786.","DOI":"10.3390\/s150408764"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"R.Jurdak X.R.Wang O.Obst and P.Valencia Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies Intelligent Systems Reference Library (2011) 309\u2013325.","DOI":"10.1007\/978-3-642-17931-0_12"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aeue.2015.12.016"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"T.S.Kim J.H.Cho and J.T.Kim Mobile Motion Sensor-Based Human Activity Recognition and Energy Expenditure Estimation in Building Environments Smart Innovations Systems and Technology (2013) 987\u2013993.","DOI":"10.1007\/978-3-642-36645-1_87"},{"key":"e_1_3_1_26_2","unstructured":"M.Kose O.D.Incel and C.Ersoy Online Human Activity Recognition on Smart Phones in Proceedings of the workshop on Mobile Sensing: From Smart phones and Wearables to Big Data Beijing China (2012) pp. 11\u201315."},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"O.Lara and M.Labrador A mobile platform for real-time Human Activity Recognition Proceedings of the IEEE Consumer Communications and Networking Conference Las Vegas USA (2012) pp. 667\u2013671.","DOI":"10.1109\/CCNC.2012.6181018"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","unstructured":"L.Lu and C.qing-Ling and Z.Yi-Ju Activity Recognition in Smart Homes Multimedia Tools Appl 2016 doi: 10.1007\/s11042-016-4197-1","DOI":"10.1007\/s11042-016-4197-1"},{"key":"e_1_3_1_29_2","first-page":"1","article-title":"Data Mining Techniques for Wireless Sensor Networks: A Survey","author":"Mahmood A.","year":"2013","unstructured":"A.Mahmood, K.Shi, S.Khatoon and M.Xiao, Data Mining Techniques for Wireless Sensor Networks: A Survey, Journal of Distributed Sensor Networks (2013), 1\u201324.","journal-title":"Journal of Distributed Sensor Networks"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"A.Mannini and A.M.Sabatini Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers Sensors (2010) 1154\u20131175.","DOI":"10.3390\/s100201154"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"E.A.Mosabbeb R.Cabral De la F.Torre and M.Fathy Multi-label Discriminative Weakly-Supervised Human Activity Recognition and Localization in the proceedings of Computer Vision - ACCV 2014. Springer International Publishing (2014) pp. 241\u2013258.","DOI":"10.1007\/978-3-319-16814-2_16"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-163329"},{"key":"e_1_3_1_33_2","first-page":"1","article-title":"Feature Selection for Activity Recognition from Smartphone Accelerometer Data Feature Selection for Activity Recognition from Smartphone Accelerometer Data","author":"Quiroz J.C.","year":"2017","unstructured":"J.C.Quiroz, A.Banerjee, S.M.Dascalu and S.L.Lau, Feature Selection for Activity Recognition from Smartphone Accelerometer Data Feature Selection for Activity Recognition from Smartphone Accelerometer Data, Journal of Intelligent Automation & Soft Computing (2017), 1\u20139.","journal-title":"Journal of Intelligent Automation & Soft Computing"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2008.4599219"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.04.006"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"R.San-segundo J.M.Montero R.Barra-chicote F.Fern\u00e1ndez and J.M.Pardo Feature extraction from smartphone inertial signals for human activity segmentation Signal Processing (2016) 359\u2013372.","DOI":"10.1016\/j.sigpro.2015.09.029"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9370-y"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","unstructured":"N.Shahid I.Haider N.Saad and B.Qaisar One-class support vector machines: Analysis of outlier detection for wireless sensor networks in harsh environments Artificial Intelligence Review 2013. doi: 10.1007\/s10462-013-9395-x","DOI":"10.1007\/s10462-013-9395-x"},{"key":"e_1_3_1_39_2","first-page":"41","article-title":"Nearest Neighbour Classification for Wireless Sensor Network Data","author":"Sharma K.","year":"2011","unstructured":"K.Sharma, M.Rajpoot and L.K.Sharma, Nearest Neighbour Classification for Wireless Sensor Network Data, Journal of Computer Trends and Technology (2011), 41\u201343.","journal-title":"Journal of Computer Trends and Technology"},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","unstructured":"Sheng Q.Li W.Mao and W.Jin Outlier detection in sensor networks Proc. of MobiHoc\u201907 Qu\u00e9bec Canada 2007 pp. 219\u2013228.","DOI":"10.1145\/1288107.1288137"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/s150102059"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"J.Su Y.Long X.Qiu S.Li and D.Liu Anomaly Detection of Single Sensors Using OCSVM_KNN. In Big Data Computing and Communications; Springer: Cham Switzerland (2015) pp. 217\u2013230.","DOI":"10.1007\/978-3-319-22047-5_18"},{"key":"e_1_3_1_43_2","unstructured":"S.Subramaniam T.Palpanas Papadopoulos V.Kalogeraki and D.Gunopulos Online outlier detection in sensor data using non-parametric models in Proceedings of VLDB\u201906 International conference on Very large databases Korea (2006) pp. 187\u2013198."},{"key":"e_1_3_1_44_2","first-page":"1","article-title":"Personalization in Mobile Activity Recognition System Using K- Medoids Clustering Algorithm","author":"Vo Q.V.","year":"2013","unstructured":"Q.V.Vo, M.T.Hoang and D.Choi, Personalization in Mobile Activity Recognition System Using K- Medoids Clustering Algorithm, Journal of Distributed Sensor Networks, (2013), 1\u201312. www.mathwords.com\/o\/outlier.htm","journal-title":"Journal of Distributed Sensor Networks"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"K.Yan X.You X.Ji G.Yin and F.Yang A hybrid outlier detection method for health care big data. Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing BDCloud Social Computing and Networking SocialCom and Sustainable Computing and Communications SustainCom (2016) pp. 157\u2013162.","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.34"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"N.A.Yousri M.A.Ismail and M.S.Kamel Fuzzy outlier analysis a combined clustering - Outlier detection approach Conference Proceedings - IEEE International Conference on Systems Man and Cybernetics 2007 pp. 412\u2013418.","DOI":"10.1109\/ICSMC.2007.4413873"},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","unstructured":"G.Yu Y.Junsong and L.Zicheng Propagative hough voting for human activity 645 recognition Computer Vision-ECCV 2012 Springer Berlin Heidelberg (2012) pp. 693\u2013706.","DOI":"10.1007\/978-3-642-33712-3_50"},{"key":"e_1_3_1_48_2","first-page":"1","article-title":"An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks","author":"Yu Q.","year":"2016","unstructured":"Q.Yu, L.Jibin and L.Jiang, An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks, Journal of Distributed Sensors Networks (2016), 1\u20139.","journal-title":"Journal of Distributed Sensors Networks"},{"key":"e_1_3_1_49_2","doi-asserted-by":"crossref","unstructured":"Y.Zhang N.Meratnia and P.Havinga Outlier Detection Techniques for Wireless Sensor Networks: A Survey IEEE Transactions on Communications and Surveys (2010) pp. 159\u2013170.","DOI":"10.1109\/SURV.2010.021510.00088"},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","unstructured":"K.Zhao J.Du C.Li C.Zhang H.Liu and C.Xu Healthy: A Diary System Based on Activity Recognition Using Smartphone in Proceedings of Mobile Ad-Hoc and Sensor Systems (MASS)\u201d Hangzhou China (2013) pp. 290\u2013294.","DOI":"10.1109\/MASS.2013.14"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","unstructured":"K.Zhang S.Shi H.Gao and Li Unsupervised outlier detection in sensor networks using aggregation tree in Proc of ADMA-Advanced Data Mining and Applications Harbin China (2007) pp. 158\u2013169.","DOI":"10.1007\/978-3-540-73871-8_16"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/2594473.2594476"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-181315","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-181315","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-181315","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:33Z","timestamp":1777455513000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-181315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,10]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7,9]]}},"alternative-id":["10.3233\/JIFS-181315"],"URL":"https:\/\/doi.org\/10.3233\/jifs-181315","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,10]]}}}