TY - JOUR
T1 - Exploring the parameter space of human activity recognition with mobile devices
AU - Saylam, Berrenur
AU - Shoaib, Muhammad
AU - Durmaz Incel, Özlem
N1 - Publisher Copyright:
© TÜBİTAK
PY - 2020/5/4
Y1 - 2020/5/4
N2 - Motion sensors available on smart phones make it possible to recognize human activities. Accelerometer, gyroscope, magnetometer, and their various combinations are used to classify, particularly, locomotion activities, ranging from walking to biking. In most of the studies, the focus is on the collection of data and on the analysis of the impact of different parameters on the recognition performance. The parameter space includes the types of sensors used, features, classification algorithms, and position/orientation of the mobile device. In most of the studies, the impact of some of these parameters is partially analyzed; however, in this work, we investigate the parameter space in detail with a global focus. Particularly, we investigate the impact of using different feature-sets, the impact of using different sensors individually and in combination, the impact of different classifiers, and the impact of phone position. Using an ANOVA analysis, we investigate the importance of various parameters on the recognition performance. We show that these parameters are ranked according to their impact on the recognition performance in the following order: sensor, position, classifier, feature. We believe that such an analysis is important since we can statistically show how much a parameter is affecting the recognition performance. Our observations can be used in future studies by only focusing on the important parameters. We present our findings as a discussion to guide the further studies in this domain.
AB - Motion sensors available on smart phones make it possible to recognize human activities. Accelerometer, gyroscope, magnetometer, and their various combinations are used to classify, particularly, locomotion activities, ranging from walking to biking. In most of the studies, the focus is on the collection of data and on the analysis of the impact of different parameters on the recognition performance. The parameter space includes the types of sensors used, features, classification algorithms, and position/orientation of the mobile device. In most of the studies, the impact of some of these parameters is partially analyzed; however, in this work, we investigate the parameter space in detail with a global focus. Particularly, we investigate the impact of using different feature-sets, the impact of using different sensors individually and in combination, the impact of different classifiers, and the impact of phone position. Using an ANOVA analysis, we investigate the importance of various parameters on the recognition performance. We show that these parameters are ranked according to their impact on the recognition performance in the following order: sensor, position, classifier, feature. We believe that such an analysis is important since we can statistically show how much a parameter is affecting the recognition performance. Our observations can be used in future studies by only focusing on the important parameters. We present our findings as a discussion to guide the further studies in this domain.
KW - Human activity recognition
KW - Machine learning
KW - Motion sensors
KW - Wearable computing
UR - http://www.scopus.com/inward/record.url?scp=85102531798&partnerID=8YFLogxK
U2 - 10.3906/ELK-1910-139
DO - 10.3906/ELK-1910-139
M3 - Article
AN - SCOPUS:85102531798
SN - 1300-0632
VL - 28
SP - 3094
EP - 3110
JO - Turkish journal of electrical engineering and computer sciences
JF - Turkish journal of electrical engineering and computer sciences
IS - 6
ER -