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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_31Scopus 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: 2024-08-16Bibliographically approved

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Caramaschi, SaraOlsson, Carl MagnusSalvi, Dario

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Caramaschi, SaraBezançon, JérémyOlsson, Carl MagnusSalvi, Dario
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