Accelerometer signal processing for user activity detection

Abstract

Estimation of human motion states is important enabling technologies for realizing a pervasive computing environment. In this paper, an improved method for estimating human states from accelerometer data is introduced. Our method for estimating human motion state utilizes various statistics of accelerometer data, such as mean, standard variation, skewness, kurtosis, eccentricity, as features for classification, and is expected to be more robust than other existing methods that rely on only a few simple statistics. A series of experiments for testing the effectiveness of the proposed method has been performed, and its result is presented.