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Article type: Research Article
Authors: Yu, Jie | Zhang, Jubin; *
Affiliations: Department of Physical Education, North China Institute of Aerospace Engineering, Langfang, Hebei, China
Correspondence: [*] Corresponding author. Jubin Zhang, Department of Physical Education, North China Institute of Aerospace Engineering, Langfang, Hebei, 065000, China. Eimail: [email protected].
Abstract: The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.
Keywords: IoT, machine learning, internet of healthcare things, healthcare monitoring, CNN, LSTM
DOI: 10.3233/JIFS-237483
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8497-8512, 2024
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