{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:58:45Z","timestamp":1773039525629,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,9,22]],"date-time":"2019-09-22T00:00:00Z","timestamp":1569110400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2019,9,22]]},"abstract":"<jats:p>Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called \u201cMobiAct.\u201d Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.<\/jats:p>","DOI":"10.1155\/2019\/9507938","type":"journal-article","created":{"date-parts":[[2019,9,22]],"date-time":"2019-09-22T19:31:24Z","timestamp":1569180684000},"page":"1-13","source":"Crossref","is-referenced-by-count":83,"title":["A Real-Time Patient Monitoring Framework for Fall Detection"],"prefix":"10.1155","volume":"2019","author":[{"given":"Dharmitha","family":"Ajerla","sequence":"first","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston K7L 2N8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4222-5702","authenticated-orcid":true,"given":"Sazia","family":"Mahfuz","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston K7L 2N8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3326-0875","authenticated-orcid":true,"given":"Farhana","family":"Zulkernine","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston K7L 2N8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"4","doi-asserted-by":"publisher","DOI":"10.1097\/00007611-199509000-00006"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2006.09.012"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2008.01.003"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2010.07.005"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2012.08.003"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925x-11-9"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2016.10.014"},{"key":"14","first-page":"145","volume-title":"Human fall detection from acceleration measurements using a recurrent neural network","year":"2018"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph15030498"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1109\/titb.2009.2033673"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2014.2298467"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.04.110"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.4018\/ijmstr.2014010103"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1504\/ijsnet.2016.076726"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2019\/9507938.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2019\/9507938.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2019\/9507938.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,22]],"date-time":"2019-09-22T19:31:25Z","timestamp":1569180685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/wcmc\/2019\/9507938\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,22]]},"references-count":15,"alternative-id":["9507938","9507938"],"URL":"https:\/\/doi.org\/10.1155\/2019\/9507938","relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"value":"1530-8669","type":"print"},{"value":"1530-8677","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,22]]}}}