Gesture recognition for interest detection in mobile applications

Gestures are a fast and efficient mean to transmit information. They are used in a large number of situations where speaking is not as effective or even not possible, such as to indicate precisely a point of interest or to warn about a danger in a noisy environment. Furthermore, gestures can also be used for intuitive human-computer interfaces where specific tasks would otherwise require navigating through graphical interface menus. Consequently, solutions to provide reliable and accurate gesture recognition have been investigated extensively in the past years. In this paper, we propose a gesture recognition system to detect user interest with a sensor-embedded mobile phone. Specifically, this system uses hidden Markov models to recognize pointing gestures. Once such a gesture has been recognized, it is straightforward to identify the point of interest based on the user location and the phone orientation. In a subject-independent scenario, we obtained a recognition accuracy above 91% with the accelerometer when discriminating between pointing gestures and similar gestures that are common with a mobile phone (e.g. looking at the screen). When using the gyroscope in addition to the accelerometer, the accuracy raised above 98%.