Improving Remote Patient Monitoring Systems Using a Fog-based IoT Platform with Speech Recognition

TL;DR

This paper presents a fog-based IoT platform that enhances remote patient monitoring by improving data management, privacy, and interactivity through speech recognition, demonstrating promising results in accuracy and performance.

Contribution

The paper introduces a novel fog-based IoT platform that addresses resource allocation, privacy, and interactivity challenges in remote patient monitoring systems, incorporating speech recognition capabilities.

Findings

Platform effectively reduces server overload

Achieves high accuracy in speech recognition

Demonstrates low latency and high throughput

Abstract

Due to the recent shortage of resources in the healthcare industry, Remote Patient Monitoring (RPM) systems arose to establish a convenient alternative for accessing healthcare services remotely. However, as the usage of this system grows with the increase of patients and sensing devices, data and network management becomes an issue. As a result, wireless architecture challenges in patient privacy, data flow, and service interactability surface that need addressing. We propose a fog-based Internet of Things (IoT) platform to address these issues and reinforce the existing RPM system. The introduced platform can allocate resources to alleviate server overloading and provide an interactive means of monitoring patients through speech recognition. We designed a testbed to simulate and test the platform in terms of accuracy, latency, and throughput. The results show the platform's potential as a viable RPM system for sound-based healthcare services.