{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T00:06:29Z","timestamp":1790985989904,"version":"4.1.0"},"reference-count":18,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2012,10,1]],"date-time":"2012-10-01T00:00:00Z","timestamp":1349049600000},"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":["Telemedicine and e-Health"],"published-print":{"date-parts":[[2012,10]]},"abstract":"<jats:p>\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">Background:<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic toggle=\"yes\">The growing population of elderly people living alone increases the need for automatic healthcare monitoring systems for elderly care. Automatic vision sensor-based systems are increasingly used for human activity recognition (HAR) in recent years. This study presents an improved model, tested using actors, of a sensor-based HAR system to recognize daily life activities of elderly people at home and generate an alert in case of abnormal HAR.<\/jats:italic>\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">Subjects and Methods:<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic toggle=\"yes\">Datasets consisting of six abnormal activities (falling backward, falling forward, falling rightward, falling leftward, chest pain, and fainting) and four normal activities (walking, rushing, sitting down, and standing up) are generated from different view angles (90\u00b0, \u221290\u00b0, 45\u00b0, \u221245\u00b0). Feature extraction and dimensions reduction are performed by R-transform followed by generalized discriminant analysis (GDA) methods. R-transform extracts symmetric, scale, and translation-invariant features from the sequences of activities. GDA increases the discrimination between different classes of highly similar activities. Silhouette sequences are quantified by the Linde\u2013Buzo\u2013Gray algorithm and recognized by hidden conditional random fields.<\/jats:italic>\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">Results:<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic toggle=\"yes\">Experimental results provide an average recognition rate of 94.2% for abnormal activities and 92.7% for normal activities.<\/jats:italic>\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">Conclusions:<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic toggle=\"yes\">The recognition rate for the highly similar activities from different view angles shows the flexibility and efficacy of the proposed abnormal HAR and alert generation system for elderly care.<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1089\/tmj.2011.0268","type":"journal-article","created":{"date-parts":[[2012,9,6]],"date-time":"2012-09-06T09:22:49Z","timestamp":1346923369000},"page":"641-647","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Model for Abnormal Activity Recognition and Alert Generation System for Elderly Care by Hidden Conditional Random Fields Using R-Transform and Generalized Discriminant Analysis Features"],"prefix":"10.1177","volume":"18","author":[{"given":"Zafar Ali","family":"Khan","sequence":"first","affiliation":[{"name":"Kyung Hee University","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Won","family":"Sohn","sequence":"additional","affiliation":[{"name":"Kyung Hee University","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2012,10,1]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1998.0744"},{"key":"e_1_3_2_3_2","unstructured":"Population Division Department of Economic and Social Affairs United Nations Secretariat. World population prospects: The 2010 revision. http:\/\/esa.un.org\/unpd\/wpp\/index.htm. 2011 November 16."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:JOHE.0000016717.22032.03"},{"key":"e_1_3_2_5_2","first-page":"8","article-title":"Laboratory diagnosis of acute myocardial infection","volume":"3","author":"Ruseva A","year":"2005","unstructured":"Ruseva A. Laboratory diagnosis of acute myocardial infection. Trakia J Sci, 2005; 3:8\u201314.","journal-title":"Trakia J Sci"},{"key":"e_1_3_2_6_2","volume-title":"The fainting phenomenon: Understanding why people faint and what to do about it","author":"Blair PG","year":"2007","unstructured":"Blair PG. The fainting phenomenon: Understanding why people faint and what to do about it. 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Conf Proc 6th Int'l Conf Adv Info Mgmt Serv, 2010; 253\u2013258."},{"key":"e_1_3_2_15_2","first-page":"246","article-title":"Adaptive background mixture models for real-time tracking","volume":"2","author":"Stauffer C","year":"1999","unstructured":"Stauffer C, , Grimson W. Adaptive background mixture models for real-time tracking. Conf Proc Comput Vision Pattern Recog, 1999; 2:246\u2013252.","journal-title":"Conf Proc Comput Vision Pattern Recog"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOM.1980.1094577"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/02763890802097045"},{"key":"e_1_3_2_19_2","first-page":"I-341","article-title":"Abnormal activity recognition in office based on R-transform","volume":"1","author":"Wang Y","year":"2007","unstructured":"Wang Y, , Huang K, , Tan T. Abnormal activity recognition in office based on R-transform. 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