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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003676naa a2200445 4500
001oai:DiVA.org:lnu-119167
003SwePub
008230208s2022 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1191672 URI
0247 a https://doi.org/10.3390/s220413772 DOI
040 a (SwePub)lnu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Arikumar, K. S.u St. Joseph’s Institute of Technology, India4 aut
2451 0a FL-PMI :b Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
264 c 2022-02-11
264 1b MDPI,c 2022
338 a electronic2 rdacarrier
520 a Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person's movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
650 7a NATURVETENSKAPx Biologix Bioinformatik och systembiologi0 (SwePub)106102 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciencesx Bioinformatics and Systems Biology0 (SwePub)106102 hsv//eng
653 a Health Informatics
653 a Hälsoinformatik
7001 a Prathiba, Sahaya Beniu Anna University, India4 aut
7001 a Alazab, Mamounu Charles Darwin University, Australia4 aut
7001 a Gadekallu, Thippa Reddyu Vellore Institute of Technology, India4 aut
7001 a Pandya, Sharnil,c Researcher,d 1984-u Symbiosis International (Deemed) University, India4 aut0 (Swepub:lnu)shpaaa
7001 a Khan, Javed Masoodu King Saud University, Saudi Arabia4 aut
7001 a Moorthy, Rajalakshmi Shenbagau Sri Ramachandra Institute of Higher Education and Research, India4 aut
7102 a St. Joseph’s Institute of Technology, Indiab Anna University, India4 org
7730 t Sensorsd : MDPIg 22:4q 22:4x 1424-8220
8564 u https://doi.org/10.3390/s22041377y Fulltext
8564 u https://lnu.diva-portal.org/smash/get/diva2:1735217/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://www.mdpi.com/1424-8220/22/4/1377/pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-119167
8564 8u https://doi.org/10.3390/s22041377

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