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Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Computer Engineering, School of Technology and Business Studies, Dalarna University, Dalarna, Sweden.
Örebro University, Örebro University School of Business.ORCID iD: 0000-0002-2372-4226
Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
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2019 (English)In: AIME 2019: Artificial Intelligence in Medicine / [ed] Riaño D., Wilk S., ten Teije A., Springer, 2019, Vol. 11526, p. 420-424Conference paper, Published paper (Refereed)
Abstract [en]

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 11526, p. 420-424
Series
Lecture Notes in Computer Science ; 11526
Keywords [en]
Unsupervised learning, Motion sensor, Parkinson’s disease, Objective evaluation, Patient monitoring, Bradykinesia, Dyskinesia
National Category
Computer and Information Sciences
Research subject
Informatics
Identifiers
URN: urn:nbn:se:oru:diva-74749DOI: 10.1007/978-3-030-21642-9_52ISI: 000495606500052Scopus ID: 2-s2.0-85068314091ISBN: 978-3-030-21642-9 (print)OAI: oai:DiVA.org:oru-74749DiVA, id: diva2:1327902
Conference
17th Conference on Artificial Intelligence in Medicine (AIME 2019), Poznan, Poland, June 26–29, 2019
Funder
Knowledge FoundationVinnova
Note

Funding Agency:

Slovenian Research Agency (ARRS) under the Artificial Intelligence and Intelligent Systems Programme (ARRS)  P2-0209

Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-11-27Bibliographically approved

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Memedi, Mevludin

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