July 28, 2019 Conference paper Open Access

Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Sevasti Bostanjopoulou; Zoe Katsarou; Lisa Klingelhoefer; Simone Mayer; Heinz Reichmann; Sofia B. Dias; José A. Diniz; Dhaval Trivedi; Ray K. Chaudhuri; Leontios J. Hadjileontiadis

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    "description": "<p>Parkinson&rsquo;s Disease (PD) is the second most&nbsp;common neurodegenerative disorder worldwide, causing both&nbsp;motor and non-motor&nbsp; symptoms. In the early stages, symptoms&nbsp;are mild and patients may ignore their existence. As a result,&nbsp;they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay,&nbsp;analysis of data passively&nbsp; captured from user&rsquo;s interaction with&nbsp;consumer technologies has been recently explored towards&nbsp;remote screening of early PD motor signs. In the current study,&nbsp;a smartphone-based method analyzing subjects&rsquo; finger&nbsp;interaction with the smartphone screen is developed for the&nbsp;quantification of fine-motor skills decline in early PD using&nbsp;Convolutional Neural Networks. Experimental results from the&nbsp;analysis of keystroke typing in-the-clinic data from 18 early PD&nbsp;patients and 15 healthy controls have shown a classification<br>\nperformance of 0.89 Area Under the Curve (AUC) with 0.79/0.79&nbsp;sensitivity/specificity, respectively. Evaluation of the&nbsp;generalization ability of the proposed approach was made by its&nbsp;application on typing data arising from a separate self-reported&nbsp;cohort of 27 PD patients&rsquo; and 84 healthy controls&rsquo; daily usage&nbsp;with their personal smartphones (data in-the-wild), achieving&nbsp;0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The&nbsp;results show the potentiality of the proposed approach to process&nbsp;keystroke dynamics arising from users&rsquo; natural typing activity&nbsp;to detect PD, which contributes to the development of digital&nbsp;tools for remote pathological symptom screening.</p>", 
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    "title": "Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks", 
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        "title": "Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS", 
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        "orcid": "0000-0002-6854-5942", 
        "affiliation": "Aristotle University of Thessaloniki", 
        "name": "Dimitrios Iakovakis"
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        "affiliation": "Aristotle University of Thessaloniki", 
        "name": "Stelios Hadjidimitriou"
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      {
        "affiliation": "Aristotle University of Thessaloniki", 
        "name": "Vasileios Charisis"
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      {
        "affiliation": "Department of Neurology, Hippokration Hospital", 
        "name": "Sevasti Bostanjopoulou"
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        "affiliation": "Department of Neurology, Hippokration Hospital Thessaloniki", 
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        "affiliation": "Department of Neurology Technical University Dresden", 
        "name": "Lisa Klingelhoefer"
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        "affiliation": "Department of Neurology Technical University Dresden", 
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        "affiliation": "International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom", 
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        "affiliation": "International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom", 
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        "affiliation": "Aristotle University of Thessaloniki", 
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