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