{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T21:22:19Z","timestamp":1773004939949,"version":"3.50.1"},"reference-count":39,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":53,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Wireless Sensor Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Elderly people represent a vulnerable population requiring continuous, reliable and timely health monitoring to maintain quality of life and reduce medical risks. Although existing wireless body sensor network (WBSN)\u2010based systems primarily focus on energy efficiency, limited attention has been given to optimising time efficiency and real\u2010time decision\u2010making performance. This study proposes an elderly health monitoring system based on WBSN using an actor\u2010critic deep reinforcement learning (ACDRL) framework to address this gap. The system utilises physiological state parameters, including heart rate, body temperature and oxygen saturation, to dynamically optimise monitoring and data transmission strategies. Device validation experiments demonstrated high sensing accuracy with a mean absolute percentage error (MAPE) of 0.68%. Model optimisation results indicate that a discount factor of 0.4 yields the best performance, achieving a mean absolute error (MAE) of 0.0401. Comparative evaluations against deep Q\u2010network (DQN)\u2010based, deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms show that the proposed ACDRL model consistently outperforms existing approaches across mean absolute error (MAE), integral absolute error (IAE) and integral squared error (ISE) metrics. Furthermore, real\u2010world WBSN implementation involving multiple sensor nodes confirms that the proposed method significantly reduces time consumption, recording 1104\u00a0ms with 10 nodes lower than all benchmark models. These results demonstrate that the proposed ACDRL\u2010based WBSN framework provides a scientifically validated, time\u2010efficient and scalable solution for real\u2010time elderly health monitoring, contributing to the advancement of intelligent healthcare systems.<\/jats:p>","DOI":"10.1049\/wss2.70023","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:51:52Z","timestamp":1772995912000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimising the Wireless Body Sensors Network (WBSN) of Elderly Health Monitoring System Through Actor\u2010Critic Mechanism"],"prefix":"10.1049","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7437-6751","authenticated-orcid":false,"given":"Indra Griha Tofik","family":"Isa","sequence":"first","affiliation":[{"name":"Department of Informatics Management Politeknik Negeri Sriwijaya  Palembang Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8845-7202","authenticated-orcid":false,"given":"Muhammad Imam","family":"Ammarullah","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Faculty of Engineering Universitas Diponegoro  Semarang Indonesia"},{"name":"Bioengineering and Environmental Sustainability Research Centre University of Liberia  Monrovia Liberia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-008X","authenticated-orcid":false,"given":"Adhan","family":"Efendi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Education, Faculty of Engineering Universitas Negeri Surabaya  Surabaya Indonesia"},{"name":"Graduate Institute of Precision Manufacturing National Chin\u2010Yi University of Technology  Taichung Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2047-4585","authenticated-orcid":false,"given":"Jasmine Nurul","family":"Izza","sequence":"additional","affiliation":[{"name":"Department of Biology, Faculty of Mathematics and Natural Sciences Universitas Negeri Malang  Malang Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6142-3072","authenticated-orcid":false,"given":"Yohanes Sinung","family":"Nugroho","sequence":"additional","affiliation":[{"name":"Department of Aeronautical Engineering Politeknik Negeri Bandung  Bandung Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8265-1314","authenticated-orcid":false,"given":"Hamid","family":"Nasrullah","sequence":"additional","affiliation":[{"name":"Automotive Engineering Vocational Study Program Politeknik Piksi Ganesha Indonesia  Kebumen Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9430-7247","authenticated-orcid":false,"given":"Febie","family":"Elfaladonna","sequence":"additional","affiliation":[{"name":"Department of Informatics Management Politeknik Negeri Sriwijaya  Palembang Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0628-9189","authenticated-orcid":false,"given":"Sigit","family":"Purnomo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Education, Faculty of Teacher Training and Education Universitas Sarjanawiyata Tamansiswa  Yogyakarta Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-9022","authenticated-orcid":false,"given":"Abdulfatah Abdu","family":"Yusuf","sequence":"additional","affiliation":[{"name":"Bioengineering and Environmental Sustainability Research Centre University of Liberia  Monrovia Liberia"},{"name":"Department of Mechanical Engineering College of Engineering University of Liberia  Monrovia Liberia"}]}],"member":"265","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.03.019"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20102826"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25061735"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/hsr2.70498"},{"key":"e_1_2_15_6_1","unstructured":"\u201cAgeing and Health \u201d accessed April 14 2025. 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