Accelerography: Feasibility of Gesture Typing using Accelerometer
This addresses gesture typing feasibility for users, but it is incremental as it builds on existing gesture recognition methods.
The paper tackled the problem of constructing and recognizing gestures for the entire English alphabet using accelerometer data, achieving an average accuracy of 97.33%.
In this paper, we aim to look into the feasibility of constructing alphabets using gestures. The main idea is to construct gestures, that are easy to remember, not cumbersome to reproduce and easily identifiable. We construct gestures for the entire English alphabet and provide an algorithm to identify the gestures, even when they are constructed continuously. We tackle the problem statistically, taking into account the problem of randomness in the hand movement gestures of users, and achieve an average accuracy of 97.33% with the entire English alphabet.