https://scholars.lib.ntu.edu.tw/handle/123456789/607443
Title: | MHealth Technologies towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review | Authors: | Zhang H Song C Rathore A.S MING-CHUN HUANG Zhang Y Xu W. |
Keywords: | 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 | Issue Date: | 2021 | Journal Volume: | 14 | Start page/Pages: | 71-81 | Source: | IEEE Reviews in Biomedical Engineering | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085150694&doi=10.1109%2fRBME.2020.2991813&partnerID=40&md5=ed0f6ec4f1ed3a3fb3bf8b49f45f057c https://scholars.lib.ntu.edu.tw/handle/123456789/607443 |
ISSN: | 19373333 | DOI: | 10.1109/RBME.2020.2991813 |
Appears in Collections: | 資訊工程學系 |
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