Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Usability often plays a key role when software is brought to market, including clearly structured workows, the way of presenting information to the user, and, last but not least, how he interacts with the application. In this context, input devices as 3D cameras or (multi-)touch displays became omnipresent in order to dene new intuitive ways of user interaction. State-of-the-art systems tightly couple application logic with separate gesture detection components for supported devices. Hard-coded rules or static models obtained by applying machine learning algorithms on many training samples are used in order to robustly detect a pre-dened set of gesture patterns. If possible at all, it becomes dicult to extend these sets with new patterns or to modify existing ones { dicult for both, application developers and end users. Further, adding gesture support for legacy software or for additional devices becomes dicult with this hardwired approach. In previous research  demonstrated how the database community can contribute to this challenge by leveraging complex event processing on data streams to express gesture patterns. While this declarative approach decouples application logic from gesture detection components, its major drawback was the non-intuitive denition of gesture queries. ,  present an approach that is related to density-based clustering in order to nd declarative gesture descriptions using only a few samples.  demonstrate the algorithms on mining denitions for multi-dimensional gestures from the sensor data stream that is delivered by a Microsoft Kinect 3D camera, and provide a way for non-expert users to intuitively customize gesturecontrolled user interfaces { even during runtime.
['Felix Beier', 'Nedal Alaqraa', 'Yuting Lai', 'Kai-Uwe Sattler']
I am writing a research paper on a idea Learning Event Patterns for Gesture Detection.
[{"from": "human", "value": "I am writing a research paper on a idea Learning Event Patterns for Gesture Detection."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Usability often plays a key role when software is brought to market, including clearly structured workows, the way of presenting information to the user, and, last but not least, how he interacts with the application. In this context, input devices as 3D cameras or (multi-)touch displays became omnipresent in order to dene new intuitive ways of user interaction. State-of-the-art systems tightly couple application logic with separate gesture detection components for supported devices. Hard-coded rules or static models obtained by applying machine learning algorithms on many training samples are used in order to robustly detect a pre-dened set of gesture patterns. If possible at all, it becomes dicult to extend these sets with new patterns or to modify existing ones { dicult for both, application developers and end users. Further, adding gesture support for legacy software or for additional devices becomes dicult with this hardwired approach. In previous research  demonstrated how the database community can contribute to this challenge by leveraging complex event processing on data streams to express gesture patterns. While this declarative approach decouples application logic from gesture detection components, its major drawback was the non-intuitive denition of gesture queries. ,  present an approach that is related to density-based clustering in order to nd declarative gesture descriptions using only a few samples.  demonstrate the algorithms on mining denitions for multi-dimensional gestures from the sensor data stream that is delivered by a Microsoft Kinect 3D camera, and provide a way for non-expert users to intuitively customize gesturecontrolled user interfaces { even during runtime."}]