TY - JOUR
T1 - Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient
AU - RAZA, MOHSIN
AU - AWAIS, MUHAMMAD
AU - Singh, Nishant
AU - Imran, Muhammad
AU - Hussain, Sajjad
PY - 2020/9/3
Y1 - 2020/9/3
N2 - 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.
AB - 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.
KW - Parkinson's disease
KW - IoT infrastructure
KW - machine learning
KW - probability of blocking
KW - low latency
KW - priority communications
UR - https://hull-repository.worktribe.com/output/3503108/intelligent-iot-framework-for-indoor-healthcare-monitoring-of-parkinsons-disease-patient
U2 - 10.1109/JSAC.2020.3021571
DO - 10.1109/JSAC.2020.3021571
M3 - Article
SP - 1
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
SN - 0733-8716
ER -