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Sensor Selection for Classification of Physical Activity in Long-Term Wearable Devices
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  • Jens Kirchner ,
  • Samira Faghih-Naini ,
  • Pinar Bisgin ,
  • Georg Fischer
Jens Kirchner
Friedrich-Alexander-Universitaet Erlangen-Nuernberg

Corresponding Author:[email protected]

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Samira Faghih-Naini
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Pinar Bisgin
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Georg Fischer
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Abstract

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.