A New Rotation Feature for Single Tri-axial Accelerometer Based 3D Spatial Handwritten Digit Recognition

Abstract

A new rotation feature extracted from tri-axial acceleration signals for 3D spatial handwritten digit recognition is proposed. The feature can effectively express the clockwise and anti-clockwise direction changes of the users’ movement while writing in a 3D space. Based on the rotation feature, an algorithm for 3D spatial handwritten digit recognition is presented. First, the rotation feature of the handwritten digit is extracted and coded. Then, the normalized edit distance between the digit and class model is computed. Finally, classification is performed using Support Vector Machine (SVM). The proposed approach outperforms time-domain features with a 22.12% accuracy improvement, peak-valley features with a 12.03% accuracy improvement, and FFT features with a 3.24% accuracy improvement, respectively. Experimental results show that the proposed approach is effective.