{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:24:29Z","timestamp":1778347469519,"version":"3.51.4"},"reference-count":10,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T00:00:00Z","timestamp":1543795200000},"content-version":"vor","delay-in-days":336,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1A6A1A03025109"],"award-info":[{"award-number":["NRF-2018R1A6A1A03025109"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>In the application of a body area network in medical healthcare, the process of receiving, archiving, and analyzing multibiosignals simultaneously from different devices for each body is very important. For example, to diagnose sleep apnea symptoms, a patient must sleep with dozens of devices, including electroencephalography (EEG), electrocardiogram (ECG), photoplethysmogram (PPG), peripheral oxygen saturation (SpO2), nasal cannula, and bands. Various wireless methods of body area network for acquisition and measurement of body signals have been introduced, but it is difficult to accurately diagnose various biosignals because their measurement frequencies are different from each other, and they are not guaranteed precise synchronization. Because each body biosignal is commonly synchronized with the heartbeat of the patient, precise synchronization of the heartbeat and other measuring cycles of each device is a critical attribute for analyzing the corelation of each biosignal in the body area network. However, it is difficult to guarantee the precise synchronization of multibiosignals by solely using carrier sense multiple access with collision detection\u2010 (CSMA\/CD\u2010) based wireless protocols, which are mainly used in existing body area networks. This study proposes a method of creating a self\u2010organizing body area network based on wireless Time Division Multiple Access (TDMA) to guarantee the synchronization of multibiosignals and compare its accuracy with the CSMA\/CD method.<\/jats:p>","DOI":"10.1155\/2018\/2394384","type":"journal-article","created":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T18:30:39Z","timestamp":1543861839000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Wireless TDMA\u2010Based Body Area Network Platform Gathering Multibiosignals Synchronized with Patient\u2019s Heartbeat"],"prefix":"10.1155","volume":"2018","author":[{"given":"Tae Min","family":"Hwang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5906-1882","authenticated-orcid":false,"given":"Seol Young","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8171-195X","authenticated-orcid":false,"given":"Soon Ju","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,12,3]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation","volume":"2","author":"Jovanov E.","year":"2005","journal-title":"Journal of Neuro Engineering and Rehabilitation"},{"key":"e_1_2_11_2_2","first-page":"47","article-title":"Wireless body area network in a ubiquitous healthcare system for physiological signal monitoring and health consulting","volume":"1","author":"Jung J.","year":"2008","journal-title":"International Journal of Signal Processing, Image Processing and Pattern Recognition"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"MarinkovicS. SpagnolC. andPopoviciE. Energy-efficient TDMA-based MAC protocol for wireless body area networks Proceedings of the 3rd International Conference on Sensor Technologies and Applications (SENSORCOMM \u203209) June 2009 Athens Greece IEEE 604\u2013609 https:\/\/doi.org\/10.1109\/sensorcomm.2009.99 2-s2.0-70449478182.","DOI":"10.1109\/SENSORCOMM.2009.99"},{"key":"e_1_2_11_4_2","doi-asserted-by":"crossref","unstructured":"BenhaddouD. BalakrishnanM. andYuanX. Remote healthcare monitoring system architecture using sensor networks Proceedings of the 2008 IEEE Region 5 Conference April 2008 1\u20136.","DOI":"10.1109\/TPSD.2008.4562760"},{"key":"e_1_2_11_5_2","unstructured":"KhanJ. Y.andYuceM. R. Wireless body area network (WBAN) for medical applications Proceedings of the New Developments in Biomedical Engineering 2010."},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2010.05.003"},{"key":"e_1_2_11_7_2","unstructured":"SheriffN. 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