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Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
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: 0000-0002-9203-1124
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
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).ORCID iD: 0000-0003-1280-5087
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2022 (English)In: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, p. 219-222Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

Place, publisher, year, edition, pages
ACM Digital Library, 2022. p. 219-222
Keywords [en]
clinical research, m-Health, IoT
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-50618DOI: 10.1145/3494322.3494363ISI: 000936000600025Scopus ID: 2-s2.0-85127119368ISBN: 978-1-4503-8566-4 (print)OAI: oai:DiVA.org:mau-50618DiVA, id: diva2:1644505
Conference
11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland
Funder
Knowledge Foundation, 20140035Available from: 2022-03-14 Created: 2022-03-14 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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Salvi, DarioOlsson, Carl MagnusYmeri, GentCarrasco-Lopez, Carmen

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