Abstract

The increasing use of wearables in smart telehealth system led to the generation of large medical big data. Cloud and fog services leverage these data for assisting clinical procedures. IoT Healthcare has been benefited from this large pool of generated data. This paper suggests the use of low-resource machine learning on Fog devices kept close to wearables for smart telehealth. For traditional telecare systems, the signal processing and machine learning modules are deployed in the cloud that processes physiological data. This paper presents a Fog architecture that relied on unsupervised machine learning big data analysis for discovering patterns in physiological data. We developed a prototype using Intel Edison and Raspberry Pi that was tested on real-world pathological speech data from telemonitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smart watches worn by the patients with PD. Results show that proposed architecture is promising for low-resource 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.

Highlights

  • As described in [1] Fog is a new architecture for computing, storage, control and networking that brings these services closer to end users.In simple words, the decentralization of services at the edge of the network is achieved.The computation and control closer to the sensors make the concept of Fog a better alternative to the cloud.In our proposed architecture of smart Fog, we leveraged the idea of Fog for speech signal processing for telehealth monitoring

  • This paper presents a Fog Computing architecture, SmartFog that relied on unsupervised clustering for discovering patterns in pathological speech data obtained from patients with Parkinson’s disease(PD)

  • The results are shown in the form of plots.The k-means clustering analysis is done on Python programming language.The plots below show the Clusters of the speech data samples used in the analysis.Different colors represent different mutually exclusive groups

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Summary

INTRODUCTION

As described in [1] Fog is a new architecture for computing, storage, control and networking that brings these services closer to end users.In simple words, the decentralization of services at the edge of the network is achieved.The computation and control closer to the sensors make the concept of Fog a better alternative to the cloud.In our proposed architecture of smart Fog, we leveraged the idea of Fog for speech signal processing for telehealth monitoring. As authors in [3] mentions dysarthria always accompanies patients with Parkinson’s disease Characterized by the monotony of speech, reduced stress, variable rate, imprecise consonants, and a breathy and harsh voice Authors in [4] [5] suggested that extreme F0 variation and range in speakers with severe dysarthria exist. In this way, Fog device could perform "smart" decision on when to upload the data to cloud backend and when not. Both of the prototypes were used for comparative analysis of computation time Both systems were tested on real world pathological speech data from telemonitoring of patients with Parkinson’s disease. The algorithm efficiently clusters the unlabeled data into groups of similarity that was done on the fog platform.One use of this analysis can be for real time Parkinson’s phenotypic sub-groupings based on the clusters

Telehealth and Associate Challenges
Big data and Telehealth
Wearable Internet of Thing for Telehealth
Fog Computing
Feature Extraction
K-means Clustering
RESULTS & DISCUSSIONS
CONCLUSIONS
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