publication . Other literature type . Conference object . 2019

Towards unobtrusive Parkinson's disease detection via motor symptoms severity inference from multimodal smartphone-sensor data

Iakovakis, Dimitrios; Hadjidimitriou, Stelios; Charisis, Vasileios; Kyritsis, Konstantinos; Papadopoulos, Alexandros; Stadtschnitzer, Michael; Jaeger, Hagen; Dagklis, Ioannis; Bostantjopoulou, Sevasti; Katsarou, Zoe; ...
Open Access
  • Published: 01 Oct 2019
  • Publisher: Zenodo
Abstract
Objective: To provide clinically-corroborated evidence of the Parkinson’s disease (PD) diagnostic potential of machine learning-based approaches for motor symptoms severity inference via multimodal data, passively captured during the natural use of smartphones. Background: PD symptoms can be mild in the early stages and they usually go unnoticed, leaving the disease undiagnosed for years [1]. Subtle motor manifestations may start five to six years prior to PD clinical diagnosis and thereafter progress quickly [2]. Motor impairment affects daily activities and can severely impact patients’ quality over the course of the disease. Information derived from mobile el...
Funded by
EC| i-PROGNOSIS
Project
i-PROGNOSIS
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS
  • Funder: European Commission (EC)
  • Project Code: 690494
  • Funding stream: H2020 | RIA
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Zenodo
Other literature type . 2019
Provider: Datacite
ZENODO
Conference object . 2019
Provider: ZENODO
Zenodo
Other literature type . 2019
Provider: Datacite
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