Sensor Selection for Classification of Physical Activity in Long-Term Wearable Devices
preprintposted on 13.01.2021, 06:40 by Jens KirchnerJens Kirchner, Samira Faghih-Naini, Pinar Bisgin, Georg FischerGeorg 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.