Authors:
Sandra Hellmers
1
;
Lianying Peng
1
;
Sandra Lau
2
;
Rebecca Diekmann
1
;
Lena Elgert
3
;
Jürgen M. 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.
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