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Sökning: WFRF:(Khan Javed Masood) > (2022) > FL-PMI :

  • Arikumar, K. S.St. Joseph’s Institute of Technology, India (författare)

FL-PMI : Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • 2022-02-11
  • MDPI,2022
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:lnu-119167
  • https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-119167URI
  • https://doi.org/10.3390/s22041377DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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%.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Prathiba, Sahaya BeniAnna University, India (författare)
  • Alazab, MamounCharles Darwin University, Australia (författare)
  • Gadekallu, Thippa ReddyVellore Institute of Technology, India (författare)
  • Pandya, Sharnil,Researcher,1984-Symbiosis International (Deemed) University, India(Swepub:lnu)shpaaa (författare)
  • Khan, Javed MasoodKing Saud University, Saudi Arabia (författare)
  • Moorthy, Rajalakshmi ShenbagaSri Ramachandra Institute of Higher Education and Research, India (författare)
  • St. Joseph’s Institute of Technology, IndiaAnna University, India (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Sensors: MDPI22:41424-8220

Internetlänk

Hitta via bibliotek

  • Sensors (Sök värdpublikationen i LIBRIS)

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