🤖 AI Summary
To address network congestion, privacy breaches, and inefficient human–machine interaction arising from data explosion in Remote Patient Monitoring (RPM), this paper proposes a fog-enhanced IoT-based RPM architecture integrated with on-device speech recognition. The system performs real-time sensor data processing and localized privacy-preserving operations at the edge, substantially reducing cloud workload; concurrently, it incorporates a lightweight speech recognition module to enable natural-language-driven clinician–patient interaction. Its key innovation lies in the first synergistic integration of fog computing and edge-side speech understanding within RPM systems, enabling resource-adaptive scheduling and ultra-low-latency response. Experimental results demonstrate an average end-to-end latency of <120 ms, a 3.2× throughput improvement over baseline approaches, and a 96.7% accuracy in voice command recognition—validating the framework’s superior performance in real-time responsiveness, data security, and interactive usability.
📝 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.