MHealth Technologies towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review
Journal
IEEE Reviews in Biomedical Engineering
Journal Volume
14
Pages
71-81
Date Issued
2021
Author(s)
Abstract
Parkinson's disease (PD) can gradually affect people's lives thus attracting tremendous attention. Early PD detection and treatment can help control the disease progress, relief from the symptoms and improve the patients' life quality. However, the current practice of PD diagnosis is conducted in a clinical setup and administrated by a PD specialist due to the early signs of PD are not noticeable in daily life. According to the report of CDC/NIH, the diagnosed time of PD ranges from 2-10 years after onset. Therefore, a more accessible PD diagnosis approach is urgently demanded. In recent years, mobile health (for short mHealth) technology has been intensively investigated for preventive medicine, particularly in chronic disease management. Notably, many types of research have explored the possibility of using mobile and wearable personal devices to detect the symptom of PD and shown promising results. It provides opportunities for transforming early PD detection from clinical to daily life. This survey paper attempts to conduct a comprehensive review of mHealth technologies for PD detection from 2000 to 2019, and compares their pros and cons in practical applications and provides insights to close the performance gap between state-of-the-art clinical approaches and mHealth technologies. ? 2008-2011 IEEE.
Subjects
body sensor networks
Mobile computing
public healthcare
Diagnosis
Disease control
mHealth
Partial discharges
Patient treatment
Chronic disease management
Current practices
Disease detection
Life qualities
Performance gaps
Personal devices
Preventive medicines
State of the art
Diseases
balance disorder
classifier
gait disorder
handwriting
human
mobile application
monitoring
mood disorder
motor dysfunction
Parkinson disease
preventive medicine
Review
sleep disorder
biomedical engineering
devices
electronic device
machine learning
physiologic monitoring
telemedicine
Biomedical Engineering
Humans
Machine Learning
Monitoring, Physiologic
Parkinson Disease
Telemedicine
Wearable Electronic Devices
Type
review