{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:12:23Z","timestamp":1773393143081,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,2,22]],"date-time":"2013-02-22T00:00:00Z","timestamp":1361491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users\u201d actions to gain knowledge about their habits and preferences.<\/jats:p>","DOI":"10.3390\/s130202682","type":"journal-article","created":{"date-parts":[[2013,2,22]],"date-time":"2013-02-22T11:21:34Z","timestamp":1361532094000},"page":"2682-2699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes"],"prefix":"10.3390","volume":"13","author":[{"given":"Iram","family":"Fatima","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea"}]},{"given":"Muhammad","family":"Fahim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea"}]},{"given":"Young-Koo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea"}]},{"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2013,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.pmcj.2011.02.007","article-title":"Activity knowledge transfer in smart environments","volume":"7","author":"Rashidi","year":"2011","journal-title":"J. Perv. Mob. Comput."},{"key":"ref_2","unstructured":"MavHome Smart Home Project. Available online: http:\/\/ailab.wsu.edu\/mavhome\/index.html (accessed on 16 February 2013)."},{"key":"ref_3","unstructured":"CASAS Smart Home Project. Available online: http:\/\/www.ailab.wsu.edu\/casas\/ (accessed on 16 February 2013)."},{"key":"ref_4","unstructured":"Aware Home. Available online: http:\/\/awarehome.imtc.gatech.edu\/ (accessed on 16 February 2013)."},{"key":"ref_5","unstructured":"The Adaptive House. Available online: http:\/\/www.cs.colorado.edu\/\u223cmozer\/index.php?dir=\/Research\/Projects\/Adaptive (accessed on 16 February 2013)."},{"key":"ref_6","unstructured":"House_n. Available online: http:\/\/architecture.mit.edu\/house_n\/ (accessed on 16 February 2013)."},{"key":"ref_7","unstructured":"Kasteren, T., Noulas, A., Englebienne, G., and Ben, K. (2008, January 21\u201324). Accurate Activity Recognition in a Home Setting. Seoul, Korea."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s00779-009-0277-9","article-title":"An activity monitoring system for elderly care using generative and discriminative models","volume":"14","author":"Kasteren","year":"2010","journal-title":"Pers. Ubiquit. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Krose, B.J.A., Kasteren, T.L.M., Gibson, C.H.S., and Dool, T. (2008, January 20\u201323). CARE: Context Awareness in Residences for Elderly. Pisa, Italy.","DOI":"10.4017\/gt.2008.07.02.083.00"},{"key":"ref_10","unstructured":"Helal, S., Kim, E., and Hossain, S. (2010, January 17\u201320). Scalable Approaches to Activity Recognition Research. Helsinki, Finland."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Scholkopf, B. (1999). Advances in Kernel Methods: Support Vector Learning, MIT Press.","DOI":"10.1049\/cp:19991092"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/11748625_21","article-title":"Modeling human behavior from simple sensors in the home","volume":"3968","author":"Aipperspach","year":"2006","journal-title":"Lect. Note. Comput. Sci."},{"key":"ref_13","first-page":"56","article-title":"Learning situation models in a smart home","volume":"39","author":"Brdiczka","year":"2009","journal-title":"IEEE Trans. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/11573425_20","article-title":"A study of detecting social interaction with sensors in a nursing home environment","volume":"3766","author":"Chen","year":"2005","journal-title":"Lect. Note. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rashidi, P., and Cook, D.J. (2010, January 13\u201317). Mining and Monitoring Patterns of Daily Routines for Assisted Living in Real World Settings. Arlington, VA, USA.","DOI":"10.1145\/1882992.1883040"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ayres, J., Gehrke, J., Yiu, T., and Flannick, J. (2002, January 23\u201326). Sequential Pattern Mining Using Bitmaps. Edmonton, Alberta, Canada.","DOI":"10.1145\/775047.775109"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TCSVT.2008.2005594","article-title":"Machine recognition of human activities: A survey","volume":"18","author":"Pavan","year":"2008","journal-title":"IEEE Trans. Circuit. Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10489-010-0216-5","article-title":"Semi-markov conditional random fields for accelerometer-based activity recognition","volume":"35","author":"Vinh","year":"2011","journal-title":"Appl. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"450","DOI":"10.5664\/jcsm.27281","article-title":"The validity of wrist actimetry assessment of sleep with and without sleep apnea","volume":"15","author":"Wang","year":"2008","journal-title":"J. Clin. Sleep Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3233\/AIS-2010-0070","article-title":"Activity recognition using semi-markov models on real world smart home datasets","volume":"2","author":"Kasteren","year":"2010","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/978-3-642-12654-3_17","article-title":"Transferring knowledge of activity recognition across sensor networks","volume":"6030","author":"Kasteren","year":"2010","journal-title":"Lect. Note. Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Van Kasteren, T.L.M., and Kr\u00f6sem, B.J.A. (2007, January 24\u201325). Bayesian Activity Recognition in Residence for Elders. Ulm, Germany.","DOI":"10.1049\/cp:20070370"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/978-3-540-73035-4_13","article-title":"HomeML: An open standard for the exchange of data within smart environments","volume":"4541","author":"Nugent","year":"2007","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TKDE.2010.148","article-title":"Discovering activities to recognize and track in a smart environment","volume":"23","author":"Rashidi","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","unstructured":"Chikhaoui, B., Wang, S., and Pigot, H. (2010, January 25\u201328). A New Algorithm Based on Sequential Pattern Mining for Person Identification. Washington, DC, USA."},{"key":"ref_26","unstructured":"Wang, Q., and Shen, Y. (2004, January 18\u201320). The Effects of Fusion Structures on Image Fusion Performances. Como, Italy."},{"key":"ref_27","unstructured":"Chen, B., Sun, F., and Hu, J. (2010, January 15\u201317). Local Linear Multi-SVM Method for Gene Function Classification. Fukuoka, Japan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.pmcj.2008.05.002","article-title":"Evidential fusion of sensor data for activity recognition in smart homes","volume":"5","author":"Hong","year":"2009","journal-title":"Perv Pervasive Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6203","DOI":"10.3390\/s8106203","article-title":"GACEM: Genetic algorithm based classifier ensemble in a multi-sensor system","volume":"8","author":"Xu","year":"2008","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/978-3-642-02710-9_19","article-title":"Workflow mining application to ambient intelligence behavior modeling","volume":"5615","year":"2009","journal-title":"Lect. Note. Comput. Sci."},{"key":"ref_31","first-page":"435","article-title":"Learning frequent behaviours of the users in intelligent environments","volume":"2","author":"Aztiria","year":"2010","journal-title":"JAISE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TSMCA.2004.838488","article-title":"A fuzzy embedded agent-based approach for realizing ambient intel-ligence in intelligent inhabited environments","volume":"35","author":"Doctor","year":"2005","journal-title":"IEEE Trans. Syst. Man Cybern. Part A"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10462-010-9160-3","article-title":"Learning patterns in ambient intelligence environments: A survey","volume":"34","author":"Aztiria","year":"2010","journal-title":"Arti. Intell. Rev."},{"key":"ref_34","first-page":"1","article-title":"Sensor-based activity recognition: A survey","volume":"99","author":"Liming","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_35","unstructured":"Mitchell, T. (1997). Machine Learning, McGraw Hill."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TITB.2009.2037317","article-title":"SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results","volume":"14","author":"Fleury","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fleury, A., Noury, N., and Vacher, M. (2009, January 3\u20136). Supervised Classification of Activities of Daily Living in Health Smart Homes Using SVM. Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5334931"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, M., Yang, J., Hao, D., and Jia, S. (2009, January 19\u201323). ECoG Recognition of Motor Imagery Based on SVM Ensemble. Guilin, China.","DOI":"10.1109\/ROBIO.2009.5420544"},{"key":"ref_39","unstructured":"Van Kasteren, T.L.M., Alemdar, H., and Cem, E. (2011, January 22\u201323). Effective Performance Metrics for Evaluating Activity Recognition Methods. Como, Italy."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/2682\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:05Z","timestamp":1760219105000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/2682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,2,22]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2013,2]]}},"alternative-id":["s130202682"],"URL":"https:\/\/doi.org\/10.3390\/s130202682","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,2,22]]}}}