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

Unpublished / Preprint


Publication Details

Author(s): Kirchner J, Faghih-Naini S, Agdemir P, Fischer G
Publication year: 2018
Language: English


Abstract

Classification of physical activity based on the kNN 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.


FAU Authors / FAU Editors

Fischer, Georg Prof. Dr.-Ing.
Professur für Technische Elektronik
Kirchner, Jens Dr.
Lehrstuhl für Technische Elektronik

Last updated on 2018-14-08 at 09:38