Abstract:
Within the popularity of new interface devices such as accelerometer based game controllers or touch-screen smartphones, the need of new accessibility options for these interfaces have become emergent. Previous studies gave the idea of using accelerometer based gesture recognition system on touch-screen smartphones with accelerometer as a new interface for visually-impaired people to use touchscreen keyboards. However, almost all studies, which have high accuracy results, are used user-dependent classifications or very limited gesture sets. In this study, our aim is to find an alternative approach to classify 20 different gestures captured by iPhone 3GS’s built-in accelerometer and make high accuracy on user-independent classifications. Our method is based on Dynamic Time Warping (DTW) with dynamic warping window sizes. The first experimental result, which is obtained from collected data set, gives 96.7% accuracy rate among 20 gestures with 1062 gesture data totally. The second experimental result, which is obtained from 4 visually-impaired people with implemented calculator as end-user test, gives 95.5% accuracy rate among 15 gestures with 720 gesture data totally. Within this work, a design of accelerometer-based recognition system is given as well as its implementation as a gesture recognition based talking calculator and experimental results.
