SmartBuckle

Abstract

Recognizing human activity is one of the most important concerns in many ubiquitous computing systems. In this paper, we present a wearable intelligence device for medical monitoring applications. We called the SmartBuckle that is designed to recognize human activity and to monitor vitality. We developed human activity recognition algorithms and evaluated them by using data acquired from a 3-axis accelerometer with embedded one image sensor in a belt. In order to evaluate, acceleration data was collected from 9 activity labels. In the image sensor, we extracted activity features based on grid-based optical flow method. In the 3-axis accelerometer sensor, we used the correlation between axes and the magnitude of the FFT for feature extraction. In the experiments, our classifiers showed the excellent performance in recognizing activities with an overall accuracy rate of 93%.