10.5281/zenodo.3675352
https://zenodo.org/records/3675352
oai:zenodo.org:3675352
Dimitrios Iakovakis
Dimitrios Iakovakis
0000-0002-6854-5942
Aristotle University of Thessaloniki
Stelios Hadjidimitriou
Stelios Hadjidimitriou
Aristotle University of Thessaloniki
Vasileios Charisis
Vasileios Charisis
Aristotle University of Thessaloniki
Konstantinos Kyritsis
Konstantinos Kyritsis
Aristotle University of Thessaloniki
Alexandros Papadopoulos
Alexandros Papadopoulos
Aristotle University of Thessaloniki
Michael Stadtschnitzer
Michael Stadtschnitzer
Fraunhofer IAIS, Schloß Birlinghoven
Hagen Jaeger
Hagen Jaeger
Fraunhofer IAIS, Schloß Birlinghoven
Ioannis Dagklis
Ioannis Dagklis
G. Papanikolaou Hospital, 3rd Neurological Clinic
Sevasti Bostantjopoulou
Sevasti Bostantjopoulou
G. Papanikolaou Hospital, 3rd Neurological Clinic
Zoe Katsarou
Zoe Katsarou
Department of Neurology, Hippokration Hospital, Thessaloniki, Greece
Lisa Klingelhoefer
Lisa Klingelhoefer
Department of Neurology, Technical University Dresden
Simone Mayer
Simone Mayer
Department of Neurology, Technical University Dresden
Heinz Reichmann
Heinz Reichmann
Department of Neurology, Technical University Dresden
Dhaval Trivedi
Dhaval Trivedi
King's College Hospital NHS Foundation Trust
Aleksandra Podlewska
Aleksandra Podlewska
King's College Hospital NHS Foundation Trust,
Alexandra Rizos
Alexandra Rizos
King's College Hospital NHS Foundation Trust,
Karrol Ray Chaudhuri
Karrol Ray Chaudhuri
King's College Hospital NHS Foundation Trust,
Anastasios Delopoulos
Anastasios Delopoulos
Aristotle University of Thessaloniki
Leontios J. Hadjileontiadis
Leontios J. Hadjileontiadis
Khalifa University of Science and Technology, Aristotle University of Thessaloniki
Towards unobtrusive Parkinson's disease detection via motor symptoms severity inference from multimodal smartphone-sensor data
Zenodo
2019
2019-10-01
Poster
10.5281/zenodo.3675351
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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 electronic sensors can provide, via algorithmic transformation, objective and dense information of an individual’s motor status, allowing for frequent relevant symptoms early screening and subsequent monitoring.
Methodology: We analyzed longitudinal recordings of tri-axial accelerometer, voice and keystroke timing data, captured passively from 70 PD patients and healthy controls (HC), in their daily life via the iPrognosis Android smartphone application, for relevant motor symptoms severity inference.
The proposed methods for motor symptoms inference show promising PD diagnostic performance in our relatively small clinically-evaluated cohorts. Our results highlight the potential of evolving these methods into an objective PD screening/monitoring tool that could support clinical diagnosis, drug response assessment and decision-making. Passive capturing of the required input data further fosters evaluation of individuals’ natural behavior, as well as long-term adherence
European Commission
10.13039/501100000780
690494
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS