February 21, 2020 Poster Open Access

Lisa Klingelhoefer; Stelios Hadjidimitriou; Anastasios Delopoulos; Fotis Karayiannis; Nikos Grammalidis; Michael Stadtschnitzer; Sevasti Bostanjopoulou; Kallol Ray Chaudhuri; Leontios Hadjileontiadis; Heinz Reichmann

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{
  "description": "<p>Mittels moderner Technologien wie Smartphones k&ouml;nnen gro&szlig;e Mengen an Daten<br>\nkontinuierlich &uuml;ber einen l&auml;ngeren Zeitraum, objektiv und ohne aktives Zutun der<br>\nNutzer im allt&auml;glichen Leben erhoben werden. Zur fr&uuml;hen Diagnosestellung des<br>\nidiopathischen Parkinsonsyndroms als langsam progrediente, chronische<br>\nErkrankung mit einschleichendem Auftreten motorischer sowie nicht-motorischer<br>\nSymptome ist dies ideal. Eine fr&uuml;here Diagnosestellung ist eine Grundvoraussetzung<br>\nzur Entwicklung neuromodulatorischer Therapien.<br>\nDas iPrognosis-Konsortium (11 Organisationen mit drei medizinischen Zentren<br>\naus 6 EU-L&auml;ndern) entwickelt eine Android Smartphone App zur Erfassung von<br>\nVerhalten und Verhaltens&auml;nderungen im Umgang mit dem Smartphone, welche<br>\nAusdruck motorischer und nicht-motorischer Symptome der Parkinsonerkrankung<br>\nsein k&ouml;nnen.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Department of Neurology, Technical University Dresden, Dresden, Germany", 
      "@type": "Person", 
      "name": "Lisa Klingelhoefer"
    }, 
    {
      "affiliation": "Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.", 
      "@type": "Person", 
      "name": "Stelios Hadjidimitriou"
    }, 
    {
      "affiliation": "Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.", 
      "@type": "Person", 
      "name": "Anastasios Delopoulos"
    }, 
    {
      "affiliation": "Microsoft Innovation Center Greece, Athens, Greece.", 
      "@type": "Person", 
      "name": "Fotis Karayiannis"
    }, 
    {
      "affiliation": "Information Technologies Institute, CERTH, Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Nikos Grammalidis"
    }, 
    {
      "affiliation": "Fraunhofer IAIS, Schlo\u00df Birlinghoven, Sankt Augustin, Germany", 
      "@type": "Person", 
      "name": "Michael Stadtschnitzer"
    }, 
    {
      "affiliation": "G. Papanikolaou Hospital, 3rd Neurological Clinic, Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Sevasti Bostanjopoulou"
    }, 
    {
      "affiliation": "King's College Hospital NHS Foundation Trust, London, United Kingdom", 
      "@type": "Person", 
      "name": "Kallol Ray Chaudhuri"
    }, 
    {
      "affiliation": "Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.", 
      "@type": "Person", 
      "name": "Leontios Hadjileontiadis"
    }, 
    {
      "affiliation": "Department of Neurology, Technical University Dresden, Dresden, Germany", 
      "@type": "Person", 
      "name": "Heinz Reichmann"
    }
  ], 
  "url": "https://zenodo.org/record/3678627", 
  "datePublished": "2020-02-21", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3678627", 
  "@id": "https://doi.org/10.5281/zenodo.3678627", 
  "@type": "CreativeWork", 
  "name": "iPrognosis \u2013 fr\u00fche Erkennung der Parkinsonerkrankung mittels Smartphone App"
}