TY  - JOUR
TI  - Comparison of methods for determining activity from physical movements
AB  - In this study, the methods which can detect the basic physical movements of a person (downward, upward, sitting, stop, walking,running) from inertial sensor (IMU) data are evaluated. The performances of classical (ANN, SVM, k-NN) and current approaches(Convolutional Neural Networks-ESA) to map IMU data to activity classes were compared. A three-stage study was carried outfor this aim: 1) data acquisition; 2) creating training/test sets; 3) construction and classification of network architectures. At thestage of data acquisition, to obtain 6 different physical movements from 10 different people, the accelerometer sensor is placed onthe persons. Repetitive movements of persons were recorded. At the second stage, the recorded long-term accelerometer data isdivided into packages in the form of short-term windows. The training set of classical approaches was constructed by featuresextracting from each packet data containing one-dimensional acceleration information. The transformation of one-dimensionalsignals to a two-dimensional image matrix for the training set of the deep learning-based approaches was performed. In the thirdstage, ANN, SVM, k-NN and CNN architectures were constructed, and classification process was carried out. As a result of theexperimental studies, it was found that the accuracy of IMU-activity mapping was 99% with the ANN method and 95% with theCNN method.
AU  - Çalışan, Mücahit
AU  - Talu, Muhammed
DO  - 10.2339/politeknik.632070
PY  - 2021
JO  - Politeknik Dergisi
VL  - 24
IS  - 1
SN  - 1302-0900
SP  - 17
EP  - 23
DB  - TRDizin
UR  - http://search/yayin/detay/417832
ER  -