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IEEESensors_Kirchner.pdf (1.19 MB)

Sensor Selection for Classification of Physical Activity in Long-Term Wearable Devices

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posted on 13.01.2021, 06:40 by Jens Kirchner, Samira Faghih-Naini, Pinar Bisgin, Georg Fischer
Classification of physical activity based on the k-NN algorithm is assessed with different combinations of sensors (from accelerometer, gyroscope, barometer) with respect to classification accuracy, power consumption and computation time. For that purpose, a wearable sensor platform is proposed and a study with 20 subjects is conducted. The combination of accelerometer and barometer is found to provide the best trade-off for the three criteria: It provides an F1 score of 94.96 ± 1.73 %, while computation time and power consumption are reduced by 45 % and 88 %, respectively, compared to the full sensor set.

History

Email Address of Submitting Author

jens.kirchner@fau.de

ORCID of Submitting Author

0000-0002-8623-9551

Submitting Author's Institution

Friedrich-Alexander-Universitaet Erlangen-Nuernberg

Submitting Author's Country

Germany