Sökning: onr:"swepub:oai:DiVA.org:lnu-119167" > FL-PMI :
Fältnamn | Indikatorer | Metadata |
---|---|---|
000 | 03676naa a2200445 4500 | |
001 | oai:DiVA.org:lnu-119167 | |
003 | SwePub | |
008 | 230208s2022 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1191672 URI |
024 | 7 | a https://doi.org/10.3390/s220413772 DOI |
040 | a (SwePub)lnu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Arikumar, K. S.u St. Joseph’s Institute of Technology, India4 aut |
245 | 1 0 | a FL-PMI :b Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems |
264 | c 2022-02-11 | |
264 | 1 | b 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 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
650 | 7 | a NATURVETENSKAPx Biologix Bioinformatik och systembiologi0 (SwePub)106102 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Biological Sciencesx Bioinformatics and Systems Biology0 (SwePub)106102 hsv//eng |
653 | a Health Informatics | |
653 | a Hälsoinformatik | |
700 | 1 | a Prathiba, Sahaya Beniu Anna University, India4 aut |
700 | 1 | a Alazab, Mamounu Charles Darwin University, Australia4 aut |
700 | 1 | a Gadekallu, Thippa Reddyu Vellore Institute of Technology, India4 aut |
700 | 1 | a Pandya, Sharnil,c Researcher,d 1984-u Symbiosis International (Deemed) University, India4 aut0 (Swepub:lnu)shpaaa |
700 | 1 | a Khan, Javed Masoodu King Saud University, Saudi Arabia4 aut |
700 | 1 | a Moorthy, Rajalakshmi Shenbagau Sri Ramachandra Institute of Higher Education and Research, India4 aut |
710 | 2 | a St. Joseph’s Institute of Technology, Indiab Anna University, India4 org |
773 | 0 | t Sensorsd : MDPIg 22:4q 22:4x 1424-8220 |
856 | 4 | u https://doi.org/10.3390/s22041377y Fulltext |
856 | 4 | u https://lnu.diva-portal.org/smash/get/diva2:1735217/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 | u https://www.mdpi.com/1424-8220/22/4/1377/pdf |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-119167 |
856 | 4 8 | u https://doi.org/10.3390/s22041377 |
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