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

Tersimpan di:
Main Authors: 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
Format: Proceeding poster
Terbitan: , 2019
Online Access: https://zenodo.org/record/3675352
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Internet

https://zenodo.org/record/3675352

Lihat Juga

  • Upper Extremity Motor Symptoms' Severity Estimation with Ecologically Valid Data Arising from Smartphone Touchscreen
    oleh: Dimitrios Iakovakis, et al.
    Terbitan: (2020)
  • i-Prognosis: Towards an early detection of Parkinson's disease via a smartphone application
    oleh: Lisa Klingelhoefer, et al.
    Terbitan: (2017)
  • Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild
    oleh: Dimitrios Iakovakis, et al.
    Terbitan: (2018)
  • Medical evaluation as gold standard to control iPrognosis application derived data for early Parkinson's disease detection
    oleh: Lisa Klingelhoefer, et al.
    Terbitan: (2020)
  • Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks
    oleh: Dimitrios Iakovakis, et al.
    Terbitan: (2019)
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