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Measuring finger dexterity in Parkinson's disease with mobile phones
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7102-083X
Tech Univ Denmark, Dept Hlth Technol, Lyngby, Denmark.
Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-4261-281X
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2024 (English)In: 2024 IEEE International Conference on Pervasive Computing and Communications: workshops and other affiliated events, percom workshops, Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 112-116Conference paper, Published paper (Refereed)
Abstract [en]

This work aims to link finger tapping and drawing tests performed on mobile phone screens with clinical ratings of Parkinson's Disease (PD). Thirty PD patients were recruited and instructed to carry out these tests in their homes. Features were extracted and used to assess the validity of the data vis a vis clinical scales (MDS-UPDRS). Statistical tests show that several features correlate with clinical scores (max correlation 0.54) and significant differences between data collected before and after medication intake (p<0.05), demonstrating the clinical validity of smartphone data. The use of Machine Learning (ML) algorithms to regress Part-3 of MDS-UPDRS further supports the validity with an absolute mean error of 6.25 (over a 0-72 scale).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 112-116
Series
IEEE Annual Conference on Pervasive Computing and Communications Workshops, ISSN 2836-5348
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:mau:diva-69978DOI: 10.1109/PerComWorkshops59983.2024.10503245ISI: 001216220000036Scopus ID: 2-s2.0-85192479973ISBN: 979-8-3503-0436-7 (print)ISBN: 979-8-3503-0437-4 (electronic)OAI: oai:DiVA.org:mau-69978DiVA, id: diva2:1886156
Conference
IEEE International Conference on Pervasive Computing and Communications (PerCom), MAR 11-15, 2024, Biarritz, FRANCE
Available from: 2024-07-30 Created: 2024-07-30 Last updated: 2024-10-30Bibliographically approved
In thesis
1. Machine Learning-Driven Analysis of Sensor Data for Objective Assessment of Parkinson's Disease Motor Symptoms in Home Environments
Open this publication in new window or tab >>Machine Learning-Driven Analysis of Sensor Data for Objective Assessment of Parkinson's Disease Motor Symptoms in Home Environments
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Parkinson’s disease (PD) is a progressive neurodegenerative brain disorder that signifi- cantly impacts quality of life for those who are affected. It is a rapidly growing condition affecting millions of people worldwide, where treatments focus on managing symptoms and slowing the degenerative process, as there are no validated treatments that can stop its progression or preemptively prevent it. Effective management of the disease relies on accurate and timely assessment of symptoms based on clinical ratings, traditionally performed through clinical examinations using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). However, in-clinic assessments are infrequent and may not capture the full spectrum of symptom fluctuations in daily life. While existing literature has focused on diagnosing PD, the current understanding falls short in terms of objectively quantifying its symptoms in daily-living conditions.

Following a design science research methodology, this thesis responds to this research gap by exploring the feasibility of using smartphones to quantify PD symptoms in a real- world, at-home setting. The research presents a cross-platform mobile application de- veloped for data collection from PD patients with the aim to identify promising system components and data types for capturing PD symptoms. Using data mining and machine learning techniques, the research explores if it is feasible to estimate the MDS-UPDRS scale based on objective measurements from smartphone-collected data. Additionally, it investigates the usability of the proposed mobile application for PD patients. By de- veloping and validating a cross-platform mobile application for symptom capturing, this thesis contributes both in terms of research results communicated in the associated peer- reviewed papers, and by providing an open source based app which makes PD symptom assessments more accessible, objective, and patient-centric.

Place, publisher, year, edition, pages
Malmö: Malmö University Press, 2024. p. 47
Series
Studies in Computer Science ; 27
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-71851 (URN)10.24834/isbn.9789178774913 (DOI)9789178774906 (ISBN)9789178774913 (ISBN)
Presentation
2024-10-16, Niagara, hörsal B2, Nordenskiöldsgatan 1, Malmö, 13:00 (English)
Opponent
Supervisors
Note

Note: The papers are not included in the fulltext online.

Paper V in dissertation as manuscript.

Available from: 2024-11-04 Created: 2024-10-30 Last updated: 2024-11-04Bibliographically approved

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Ymeri, GentOlsson, Carl MagnusSalvi, Dario

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Ymeri, GentOlsson, Carl MagnusSvenningsson, PerSalvi, Dario
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