Rotation invariant feature extraction from 3-D acceleration signals

In this paper, we propose a method to extract features from three-dimensional acceleration signals. The proposed method is based on the (auto-)correlation matrix of Fourier transform features, naturally containing the correlations between the frequencies as well as the ordinary power spectrum for each frequency. The proposed features are inherently invariant to both rotational variations and temporal shift (delay), whereas the other methods employ ad hoc preprocessing to increase robustness to those variations. Thereby, we can favorably apply the proposed method to analyze 3-D acceleration signals regardless of the orientations of the accelerometer. In the experiment on gait identification using an accelerometer embedded in a cellular phone, the proposed method outperformed the other methods.