Efficient Dynamic Time Warping for 3D Handwriting Recognition using Gyroscope Equipped Smartphones
Handwriting character recognition from 3D accelerometer data has emerged as a popular technique for natural human computer interaction. In this paper, we first review conventional 3D accelerometer-based methods and examine their shortcomings. On the basis of the information gleaned from these activities, we then propose a new 3D gyroscope-based handwriting recognition system that uses stepwise lower-bounded dynamic time warping, instead of conventional 3D accelerometer data. The results of experiments conducted indicate that our proposed method is more effective and efficient than conventional methods for user-independent recognition of the 26 lowercase letters in the English alphabet.
The dataset utilized comprised 1,500 online characters obtained from handwritten, user-independent English lowercase letters. The data were acquired from 3D accelerometer and 3D gyroscope sensors embedded in the Apple iPhone 4 and sampled at 100 Hz; six signals in 3D (xyz) coordinates were obtained for each character.