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

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, convenien...

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Autores principales: Arikumar, K. S., Prathiba, Sahaya Beni, Alazab, Mamoun, Gadekallu, Thippa Reddy, Pandya, Sharnil, Khan, Javed Masood, Moorthy, Rajalakshmi Shenbaga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962969/
https://www.ncbi.nlm.nih.gov/pubmed/35214282
http://dx.doi.org/10.3390/s22041377
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author Arikumar, K. S.
Prathiba, Sahaya Beni
Alazab, Mamoun
Gadekallu, Thippa Reddy
Pandya, Sharnil
Khan, Javed Masood
Moorthy, Rajalakshmi Shenbaga
author_facet Arikumar, K. S.
Prathiba, Sahaya Beni
Alazab, Mamoun
Gadekallu, Thippa Reddy
Pandya, Sharnil
Khan, Javed Masood
Moorthy, Rajalakshmi Shenbaga
author_sort Arikumar, K. S.
collection PubMed
description 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%.
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spelling pubmed-89629692022-03-30 FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems Arikumar, K. S. Prathiba, Sahaya Beni Alazab, Mamoun Gadekallu, Thippa Reddy Pandya, Sharnil Khan, Javed Masood Moorthy, Rajalakshmi Shenbaga Sensors (Basel) Article 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%. MDPI 2022-02-11 /pmc/articles/PMC8962969/ /pubmed/35214282 http://dx.doi.org/10.3390/s22041377 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arikumar, K. S.
Prathiba, Sahaya Beni
Alazab, Mamoun
Gadekallu, Thippa Reddy
Pandya, Sharnil
Khan, Javed Masood
Moorthy, Rajalakshmi Shenbaga
FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title_full FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title_fullStr FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title_full_unstemmed FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title_short FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
title_sort fl-pmi: federated learning-based person movement identification through wearable devices in smart healthcare systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962969/
https://www.ncbi.nlm.nih.gov/pubmed/35214282
http://dx.doi.org/10.3390/s22041377
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