Taeho Hur  Sungyoung Lee

10.5626/JOK.2018.45.9.950

Activity recognition Smartphone Smartwatch Fusion posterior probability

Traditional inertial sensor based activity recognition methods in which multiple sensor units are attached to the body is changing to accommodate the use of smart devices such as smartphones and smartwatches. In this paper, we propose a multi-level fusion activity recognition framework to recognize daily activities using smartphones and smartwatches which can be purchased easily for minimum sensor based activity recognition. The proposed framework uses various types of fusion techniques such as data fusion, feature fusion, and decision fusion. While the proposed framework does not use common methods of decision fusion such as majority voting or weighted voting, it does use posterior probability based fusion for better accuracy and confidence. Experiments are conducted to compare results between using and not using the probability and between using and not using each fusion technique. The results demonstrated the excellent performance of the proposed framework.