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An Inertial and Positioning Dataset 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
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
Oxford University Hospitals NHS Foundation Trust Oxford, United Kingdom.
Oxford University Hospitals NHS Foundation Trust Oxford, United Kingdom.
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2024 (English)In: Proceedings of 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, IEEE, 2024, p. 225-230Conference paper, Published paper (Refereed)
Abstract [en]

The 6-minute walk test is a standardized test used in healthcare to monitor the progress of diseases affecting physical capacity and function. Inertial sensors and positioning data from wearables or smartphones allow to conduct clinical tests in patients' home environments, thereby easing the burden for patients and reducing costs for healthcare. Computation of the 6-minute walked distance requires high accuracy to be clinically useful and current consumer technology-based approaches show that noise and interference in the data often misleads algorithms used to estimate the walk distance. In this research, we are sharing a dataset of inertial and positioning information from 203 walking tests collected with users' own smartphones and the respective ground truth distances. Ground truth is measured with trundle wheels of two types, one which only provides the final distance measurement, and one which provides continuous distance measurements to also capture changes in walking speed. The tests are performed by 19 individuals, both cardiovascular patients and healthy participants. We analyse the dataset using a state-of-the-art algorithm and observe algorithm results in relation to walking features. Based on this, we elaborate on for technology development that may provide further improvements in accuracy for walk distance estimation algorithms, including how data quality and reliability can be assessed.

Place, publisher, year, edition, pages
IEEE, 2024. p. 225-230
Keywords [en]
—GNSS, 6-minute walk test, Accuracy, Atmospheric measurements, data quality, dataset, Distance measurement, Estimation, IMU, Legged locomotion, Medical services, Particle measurements, Reliability, Smart phones, Wheels
National Category
Medical Laboratory Technologies
Identifiers
URN: urn:nbn:se:mau:diva-72876DOI: 10.1109/MetroXRAINE62247.2024.10796472Scopus ID: 2-s2.0-85216102078ISBN: 979-8-3503-7800-9 (electronic)ISBN: 979-8-3503-7801-6 (print)OAI: oai:DiVA.org:mau-72876DiVA, id: diva2:1923739
Conference
2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), ST Albans-London, UK, October 21-23, 2024
Available from: 2024-12-30 Created: 2024-12-30 Last updated: 2025-04-03Bibliographically 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)
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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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