Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things

TL;DR

This paper introduces a fog computing framework utilizing low-resource machine learning devices like Intel Edison and Raspberry Pi for unsupervised analysis of physiological data in wearable IoT healthcare, demonstrating promising results in Parkinson's disease monitoring.

Contribution

It presents a novel fog-based architecture for unsupervised machine learning in wearable healthcare, enabling edge processing close to data sources, reducing reliance on cloud computing.

Findings

Successful prototype implementation with Intel Edison and Raspberry Pi.

Effective unsupervised pattern discovery in Parkinson's speech data.

Potential for low-resource, real-time telehealth applications.

Abstract

The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We employed Intel Edison and Raspberry Pi as Fog computer in proposed architecture. We performed validation studies on real-world pathological speech data from in home monitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smartwatches worn by the patients with PD. Results showed that proposed architecture is promising for low-resource clinical machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.