Sensor Selection for Classification of Physical Activity in Long-Term Wearable Devices
preprintposted on 2021-01-13, 06:40 authored 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.
Email Address of Submitting Authorjens.firstname.lastname@example.org
ORCID of Submitting Author0000-0002-8623-9551
Submitting Author's InstitutionFriedrich-Alexander-Universitaet Erlangen-Nuernberg
Submitting Author's Country