Conference paper Open Access
Alexandros Papadopoulos; Konstantinos Kyritsis; Sevasti Bostanjopoulou; Lisa Klingelhoefer; Ray K. Chaudhuri; Anastasios Delopoulos
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.3676525"> <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/> <dct:type rdf:resource="http://purl.org/dc/dcmitype/Text"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.3676525</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3676525"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Alexandros Papadopoulos</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Konstantinos Kyritsis</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Sevasti Bostanjopoulou</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Department of Neurology, Hippokration Hospital, Thessaloniki, Greece</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Lisa Klingelhoefer</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Department of Neurology, Technical University of Dresden, Dresden, Germany</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Ray K. Chaudhuri</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, UK</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Anastasios Delopoulos</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued> <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/H2020/690494/"/> <schema:funder> <foaf:Organization> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier> <foaf:name>European Commission</foaf:name> </foaf:Organization> </schema:funder> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-07-31</dct:issued> <owl:sameAs rdf:resource="https://zenodo.org/record/3676525"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3676525</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3676524"/> <dct: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></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.3676525"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL>https://doi.org/10.5281/zenodo.3676525</dcat:accessURL> <dcat:byteSize>427186</dcat:byteSize> <dcat:downloadURL>https://zenodo.org/record/3676525/files/alpapado2019embc.pdf</dcat:downloadURL> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/H2020/690494/"> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">690494</dct:identifier> <dct:title>Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS</dct:title> <frapo:isAwardedBy> <foaf:Organization> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier> <foaf:name>European Commission</foaf:name> </foaf:Organization> </frapo:isAwardedBy> </foaf:Project> </rdf:RDF>
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