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Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection

Alexandros Papadopoulos; Konstantinos Kyritsis; Sevasti Bostanjopoulou; Lisa Klingelhoefer; Ray K. Chaudhuri; Anastasios Delopoulos


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{
  "description": "<p>Parkinson&rsquo;s Disease (PD) is a neurodegenerative&nbsp;disorder that manifests through slowly progressing symptoms,&nbsp;such as tremor, voice degradation and bradykinesia. Automated&nbsp;detection of such symptoms has recently received much attention&nbsp;by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately,&nbsp;most of the approaches proposed so far, operate under a strictly&nbsp;laboratory setting, thus limiting their potential applicability in&nbsp;real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem&nbsp;at hand, as a case of Multiple-Instance Learning, wherein a&nbsp;subject is represented as an unordered bag of signal segments&nbsp;and a single, expert-provided, ground-truth. We employ a&nbsp;deep learning approach that combines feature learning and a&nbsp;learnable pooling stage and is trainable end-to-end. Results on&nbsp;a newly introduced dataset of accelerometer signals collected&nbsp;in-the-wild confirm the validity of the proposed approach.&nbsp;</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Alexandros Papadopoulos"
    }, 
    {
      "affiliation": "Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Konstantinos Kyritsis"
    }, 
    {
      "affiliation": "Department of Neurology, Hippokration Hospital, Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Sevasti Bostanjopoulou"
    }, 
    {
      "affiliation": "Department of Neurology, Technical University of Dresden, Dresden, Germany", 
      "@type": "Person", 
      "name": "Lisa Klingelhoefer"
    }, 
    {
      "affiliation": "International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, UK", 
      "@type": "Person", 
      "name": "Ray K. Chaudhuri"
    }, 
    {
      "affiliation": "Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Anastasios Delopoulos"
    }
  ], 
  "headline": "Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-07-31", 
  "url": "https://zenodo.org/record/3676525", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3676525", 
  "@id": "https://doi.org/10.5281/zenodo.3676525", 
  "@type": "ScholarlyArticle", 
  "name": "Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection"
}
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