Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life - Hellmers, Sandra; Peng, Lianying; Lau, Sandra; Diekmann, Rebecca; Elgert, Lena; Bauer, Jürgen M; Hein, Andreas; Fudickar, Sebastian (Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, 2020)
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 clinicalmobility 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 the frailty status of the participants. Therefore, these scores are indicators of the physical function and hence validate the utility of the developed algorithm.