Conference paper Open Access
Alexandros Papadopoulos; Konstantinos Kyritsis; Sevasti Bostanjopoulou; Lisa Klingelhoefer; Ray K. Chaudhuri; Anastasios Delopoulos
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <controlfield tag="005">20200221072054.0</controlfield> <controlfield tag="001">3676525</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</subfield> <subfield code="a">Konstantinos Kyritsis</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Department of Neurology, Hippokration Hospital, Thessaloniki, Greece</subfield> <subfield code="a">Sevasti Bostanjopoulou</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Department of Neurology, Technical University of Dresden, Dresden, Germany</subfield> <subfield code="a">Lisa Klingelhoefer</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, UK</subfield> <subfield code="a">Ray K. Chaudhuri</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</subfield> <subfield code="a">Anastasios Delopoulos</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">427186</subfield> <subfield code="z">md5:8b36f02cf9ecb7373e613d244180f455</subfield> <subfield code="u">https://zenodo.org/record/3676525/files/alpapado2019embc.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2019-07-31</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:3676525</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Multimedia Understanding Group, Information Processing Laboratory, Dept. of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece</subfield> <subfield code="a">Alexandros Papadopoulos</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection</subfield> </datafield> <datafield tag="536" ind1=" " ind2=" "> <subfield code="c">690494</subfield> <subfield code="a">Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.3676524</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.3676525</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> </record>
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