Hand Gesture Recognition Interface for Visually Impaired and Blind People

The practical adaption of interface solutions for visual impaired and blind people is limited by simplicity and usability in practical scenarios. Different solutions (e.g. Drishti [1]) focuses upon speech or keyboard interfaces, which are not efficient or transparent in every-day environments. As an easy and practical way to achieve human-computer- interaction, in this paper hand gesture recognition was used to facilitate the reduction of hardware components. Additionally a qualitative user study was performed to compare learning curves of different subjects with and without prior knowledge of gesture recognition devices, interpreting the readings from a sensitive surface by machine learning algorithms. The user study was made using well-known machine learning algorithms applied to recognizing symbols from the graffiti handwriting system [2] and the WEKA data mining software [3] for comparing individual machine learning approaches.