Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

MOHSIN RAZA, MUHAMMAD AWAIS, Nishant Singh, Muhammad Imran, Sajjad Hussain

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Abstract

Parkinson's disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson's patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson's patients' count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient's conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson's over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson's progression. Index Terms-Internet of things (IoT), machine learning, Parkinson's disease, probability of blocking, low latency, priority communications.

Original languageEnglish
Pages (from-to)1
JournalIEEE Journal on Selected Areas in Communications
Early online date3 Sep 2020
DOIs
Publication statusE-pub ahead of print - 3 Sep 2020

Keywords

  • Parkinson's disease
  • IoT infrastructure
  • machine learning
  • probability of blocking
  • low latency
  • priority communications

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