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

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

doi:10.3390/s22041377

Abstract

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%.

Highlights

Recent advancements in Internet of Things (IoT) technologies have modernized conventional healthcare systems, e.g., via face-to-face consultations in smart healthcare (SHC) systems [1,2]
Considering the unlabeled data in the SHC environment, we evaluated the efficacy of the federated learning-based person movement identification (FL-person’s movement identification (PMI)) auto-labeling approach
All techniques produce good precision values when all the characteristics are comprised in the dataset, the federated learning (FL)-PMI outperforms with the highest precision value and achieves overall higher performance

Summary

Introduction

Recent advancements in Internet of Things (IoT) technologies have modernized conventional healthcare systems, e.g., via face-to-face consultations in smart healthcare (SHC) systems [1,2]. SHC encompasses wearable devices, IoT-enabled technology, and mobile internet connectivity. Wearable devices are electronic devices equipped with multiple sensors, which patients wear in order for their health conditions to be monitored, recorded, and analyzed [3]. SHC dynamically accesses information from wearable devices, connects with healthcare managers/clinicians, materials, and institutions, and actively manages and intelligently responds to medical ecosystem needs. Monitoring the activities/movements of patients is one of the essential responsibilities in SHC [1,4]. Sensors attached with wearable devices are used to identify the movement of a person. For health monitoring and to offer clinically valuable data for healthcare, wearable sensors are attached with wearable products or directly with the body [7]. The sensors detect the physical, chemical, or biological property quantities and convert them into readable signals. There are a lot of issues in PMI that arise due to sensor data

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Conclusion