dc.contributor.author | Chen, O | en_US |
dc.contributor.author | Lipsmeier, F | en_US |
dc.contributor.author | Phan, H | en_US |
dc.contributor.author | Prince, J | en_US |
dc.contributor.author | Taylor, K | en_US |
dc.contributor.author | Gossens, C | en_US |
dc.contributor.author | Lindemann, M | en_US |
dc.contributor.author | De Vos, M | en_US |
dc.date.accessioned | 2020-06-11T13:48:40Z | |
dc.date.available | 2020-04-07 | en_US |
dc.date.issued | 2020-04-20 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/64832 | |
dc.description.abstract | Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | IEEE Trans Biomed Eng | en_US |
dc.title | Building a Machine-learning Framework to Remotely Assess Parkinson's Disease Using Smartphones. | en_US |
dc.type | Article | |
dc.rights.holder | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/TBME.2020.2988942 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/32324537 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | PP | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |