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Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study
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
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
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
2022 (English)In: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Tsanas, Athanasios; Triantafyllidis, Andreas, Cham: Springer, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

Place, publisher, year, edition, pages
Cham: Springer, 2022.
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211
Keywords [en]
mobile health, Parkinson’s disease, mPower data
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-58646DOI: 10.1007/978-3-031-34586-9_29Scopus ID: 2-s2.0-85164108273ISBN: 978-3-031-34585-2 (print)ISBN: 978-3-031-34586-9 (electronic)OAI: oai:DiVA.org:mau-58646DiVA, id: diva2:1743257
Conference
16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-02-05Bibliographically approved

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The full text will be freely available from 2024-07-11 08:28
Available from 2024-07-11 08:28

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Ymeri, GentSalvi, DarioOlsson, Carl Magnus

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