A novel gateway-based solution for remote elderly monitoring

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

Health Monitoring System for Elderly Patients Using Intelligent Task Mapping Mechanism in Closed Loop Healthcare Environment

The ageing population’s problems directly impact countries’ socio-economic structure, as more resources are required to monitor the aged population’s health. The growth in human life expectancy is increasing due to medical technologies and nutritional science innovations. The Internet of Things (IoT) is the connectivity of physical objects called things to the Internet. IoT has a wide range of health monitoring applications based on biomedical sensing devices to monitor health conditions. This paper proposes elderly patients’ health monitoring architecture based on an intelligent task mapping approach for a closed-loop IoT healthcare environment. As a case study, a health monitoring system was developed based on the proposed architecture for elderly patients’ health monitoring in the home, ambulance, and hospital environment. The system detects and notifies deteriorating conditions to the authorities based on biomedical sensors for faster interventions. Wearable biomedical sensors are used for monitoring body temperature, heart rate, blood glucose level, and patient body position. Threshold and machine learning-based approaches were used to detect anomalies in the health sensing data. The proposed architecture’s performance analysis is evaluated in terms of round trip time, reliability, task drop rate, and latency performance metrics. Performance results show that the proposed architecture of the elderly patient health monitoring can provide reliable solutions for critical tasks in IoT environments.