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An IoT-Based Method for Collecting Reference Walked Distance for the 6-Minute Walk Test
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: 0000-0002-8461-0089
Université de Montpellier, Montpellier, France.ORCID iD: 0009-0002-2191-2204
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: 0000-0002-9203-1124
2024 (English)In: Pervasive Computing Technologies for Healthcare: 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings / [ed] Dario Salvi, Pieter Van Gorp, Syed Ahmar Shah, Springer, 2024, p. 478-489Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the need for accurate and continuous measurement of walked distance in applications such as indoor localisation, gait analysis or the 6-minute walk test (6MWT). We propose a method to continuously collect ground truth data of walked distance using an IoT-based trundle wheel. The wheel is connected via Bluetooth Low Energy to a smartphone application which allows the collection of inertial sensor data and GPS location information in addition to the reference distance. We prove the usefulness of this data collection approach in a use case where we derive walked distance from inertial data. We train a 1-dimensional CNN on inertial data collected by one researcher in 15 walking sessions of 1 km length at varying speeds. The training is facilitated by the continuous nature of the reference data. The accuracy of the algorithm is then tested on holdout data of a 6-min duration for which the error of the inferred distance is within clinically significant limits. The proposed approach is useful for the efficient collection of input and reference data for the development of algorithms used to estimate walked distance, such as for the 6MWT. 

Place, publisher, year, edition, pages
Springer, 2024. p. 478-489
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 572
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:mau:diva-70308DOI: 10.1007/978-3-031-59717-6_31ISI: 001481019900030Scopus ID: 2-s2.0-85196854728ISBN: 978-3-031-59716-9 (print)ISBN: 978-3-031-59717-6 (electronic)OAI: oai:DiVA.org:mau-70308DiVA, id: diva2:1889577
Conference
17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023
Available from: 2024-08-16 Created: 2024-08-16 Last updated: 2025-06-10Bibliographically approved
In thesis
1. Digital health technologies for unsupervised physical activity testing and monitoring
Open this publication in new window or tab >>Digital health technologies for unsupervised physical activity testing and monitoring
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Monitoring physical activity, function and capacity through Digital Health Technology (DHT) has a high potential to benefit healthcare providers and patients. Current practices of observing physical function and capacity use standard tests such as the 6-Minute Walk Test (6MWT) or the Timed Up and Go, which collect momentary information on the patient’s health status. The use of DHT is demonstrated to enhance these types of assessments, from the instrumentation of physical tests with technology to the analysis of digital biomarkers collected during one’s daily life. Research at national and international levels investigates these topics on a large scale; however, it often lacks transparency and details in regards to used algorithms and data quality. These aspects are crucial when implementing technology for health-related purposes, where data quality and methods accuracy are fundamental for impacting clinical practices. This thesis investigates how DHT can support physical testing in ecological or community environments, answering three main research questions related to data collection and quality, algorithms, and the association between daily life physical activity and physical tests. Throughout six articles, this work investigates methods for inferring walked distance during the 6MWT in indoor and outdoor conditions, highlighting the importance of data quality collected through DHT. It shares a publicly available dataset providing inertial and localization information of patients and healthy volunteers. Lastly, it reports on insights and common procedures regarding DHT used during everyday life and its relation with physical tests. Foundations are laid for future work in this domain.   

Place, publisher, year, edition, pages
Malmö: Malmö University Press, 2025. p. 40
Series
Studies in Computer Science ; 36
Keywords
Digital Health Technology, Physical Activity, 6-Minute Walk Test, Data quality, Real-world Assessment
National Category
Medical Engineering
Identifiers
urn:nbn:se:mau:diva-75110 (URN)10.24834/isbn.9789178775958 (DOI)978-91-7877-594-1 (ISBN)978-91-7877-595-8 (ISBN)
Presentation
2025-04-07, Auditorium B1, Niagara, Malmö, 10:15 (English)
Opponent
Supervisors
Note

Paper 5 and 6 i dissertation as manuscript. Not included in the full text online. 

Available from: 2025-04-03 Created: 2025-04-03 Last updated: 2025-10-10Bibliographically approved

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Caramaschi, SaraOlsson, Carl MagnusSalvi, Dario

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Citation style
  • apa
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  • de-DE
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