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Authors: Sandra Hellmers 1 ; Lianying Peng 1 ; Sandra Lau 2 ; Rebecca Diekmann 1 ; Lena Elgert 3 ; Jürgen Bauer 2 ; Andreas Hein 1 and Sebastian Fudickar 1

Affiliations: 1 Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany ; 2 Center for Geriatric Medicine, University Heidelberg, 69117 Heidelberg, Germany ; 3 Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, TU Braunschweig, 38106 Braunschweig, Germany

Keyword(s): Activity Level, Sedentary Time, Inertial Measurement Unit, Healthy Aging, Machine Learning, Functional Fitness, Functional Decline.

Abstract: The trend of an ageing population is becoming more and more obvious. Staying healthy in old age is an important social issue. Thereby, physical activity is essential for the preservation of physical function. We developed an algorithm for determining the activity level of seniors in everyday life. The proposed algorithm is based on machine learning activity detection using inertial measurement unit data. A series of activity scores is obtained by executing the algorithm from data on the type of activity, total activity time and activity intensity. To evaluate the performance of the proposed algorithm, a study with 251 participants aged above 70 (75.41 ± 3.88) years was conducted and the correlation between individual activity scores and clinical mobility assessments was determined. Results showed a relation between the Six Minute Walking Test and the total score in terms of activity level as well as the walk score. Additionally, the MVPA- and walk-score show a clear trend regarding t he frailty status of the participants. Therefore, these scores are indicators of the physical function and hence validate the utility of the developed algorithm. (More)

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Paper citation in several formats:
Hellmers, S.; Peng, L.; Lau, S.; Diekmann, R.; Elgert, L.; Bauer, J.; Hein, A. and Fudickar, S. (2020). Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, ISBN 978-989-758-398-8; ISSN 2184-4305, pages 579-585. DOI: 10.5220/0009095505790585

@conference{healthinf20,
author={Sandra Hellmers. and Lianying Peng. and Sandra Lau. and Rebecca Diekmann. and Lena Elgert. and Jürgen Bauer. and Andreas Hein. and Sebastian Fudickar.},
title={Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,},
year={2020},
pages={579-585},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009095505790585},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,
TI - Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life
SN - 978-989-758-398-8
IS - 2184-4305
AU - Hellmers, S.
AU - Peng, L.
AU - Lau, S.
AU - Diekmann, R.
AU - Elgert, L.
AU - Bauer, J.
AU - Hein, A.
AU - Fudickar, S.
PY - 2020
SP - 579
EP - 585
DO - 10.5220/0009095505790585

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