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

K. S. Arikumar, Sahaya Beni Prathiba, Mamoun Alazab, Thippa Reddy Gadekallu*, Sharnil Pandya, Javed Masood Khan, Rajalakshmi Shenbaga Moorthy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)


Recent technological developments, such as the Internet of Things (IoT), artificial intel-ligence, 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% ac-curacy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.

Original languageEnglish
Article number1377
Number of pages19
Issue number4
Publication statusPublished - 11 Feb 2022
Externally publishedYes

Cite this