Yun-Young Nam, Yoo-Joo Choi, We-Duke Cho, Journal of Internet Computing and Services, Vol. 11, No. 1, pp. 129-142, Feb. 2010
Full Text:  HTML
Keywords: Activity recognition, multi-sensor, wearable device, pattern recognition, SVM, Ubiquitous

Abstract

In this paper, we present a wearable intelligent device based on multi-sensor for monitoring human activity. In order to recognize multiple activities, we developed activity recognition algorithms utilizing an image sensor and a 3-axis accelerometer sensor. We proposed a grid?based optical flow method and used a SVM classifier to analyze data acquired from multi-sensor. We used the direction and the magnitude of motion vectors extracted from the image sensor. We computed the correlation between axes and the magnitude of the FFT with data extracted from the 3-axis accelerometer sensor. In the experimental results, we showed that the accuracy of activity recognition based on the only image sensor, the only 3-axis accelerometer sensor, and the proposed multi-sensor method was 55.57%, 89.97%, and 89.97% respectively.

Statistics
Cite this article