Poster Open Access

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

Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Konstantinos Kyritsis; Alexandros Papadopoulos; Michael Stadtschnitzer; Hagen Jaeger; Ioannis Dagklis; Sevasti Bostantjopoulou; Zoe Katsarou; Lisa Klingelhoefer; Simone Mayer; Heinz Reichmann; Dhaval Trivedi; Aleksandra Podlewska; Alexandra Rizos; Karrol Ray Chaudhuri; Anastasios Delopoulos; Leontios J. Hadjileontiadis

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

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