A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors

Abstract

Following the technological advancement and the constantly emerging assisted living applications, sensor-based activity recognition research receives great attention. Until recently, the majority of relevant research involved extracting knowledge out of single modalities, however, when individual sensors performances are not satisfactory, combining information from multiple sensors can be of use and improve the activity recognition rate. Early and late fusion classifier strategies are usually employed to successfully merge multiple sensors. This paper proposes a novel framework for combining accelerometers and gyroscopes at decision level, in order to recognize human activity. More specifically, we propose a weighted late fusion framework that utilizes the detection rate of a classifier. Furthermore, we propose the modification of an already existing class-based weighted late fusion framework. Experimental results on a publicly available and widely used dataset demonstrated that the combination of accelerometer and gyroscope under the proposed frameworks improves the classification performance.