A context aware approach for enhancing gesture recognition accuracy on handheld devices

Input capabilities (e.g. joystick, keypad) of handheld devices allow users to interact with the user interface to access the information and mobile services. However, these input capabilities are very limited because of the mobile convenience. New input devices and interaction techniques are needed for handheld devices. Gestural interaction with accelerometer sensor is one of the newest interaction techniques on mobile computing. In this thesis, we introduce solutions that can be used for automatically enhancing the gesture recognition accuracy of accelerometer sensor, and as a standardized gesture library for gestural interaction on touch screen and accelerometer sensor. In this novel solution, we propose a framework that decides on suitable signal processing techniques for acceleration sensor data for a given context of the user. First system recognizes the context of the user using pattern recognition algorithm. Then, system automatically chooses signal ltering techniques for recognized context, and recognizes gestures. Gestures are also standardized for better usage. In this work, we also present several experiments which show the feasibility and e ectiveness of our automated gesture recognition enhancement system.