Gestures à Go Go
Citations Over TimeTop 10% of 2015 papers
Abstract
Training a high-quality gesture recognizer requires providing a large number of examples to enable good performance on unseen, future data. However, recruiting participants, data collection, and labeling, etc., necessary for achieving this goal are usually time consuming and expensive. Thus, it is important to investigate how to empower developers to quickly collect gesture samples for improving UI usage and user experience. In response to this need, we introduce Gestures à Go Go ( g 3), a web service plus an accompanying web application for bootstrapping stroke gesture samples based on the kinematic theory of rapid human movements. The user only has to provide a gesture example once, and g 3 will create a model of that gesture. Then, by introducing local and global perturbations to the model parameters, g 3 generates from tens to thousands of synthetic human-like samples. Through a comprehensive evaluation, we show that synthesized gestures perform equally similar to gestures generated by human users. Ultimately, this work informs our understanding of designing better user interfaces that are driven by gestures.
Related Papers
- → An Intelligent Smart Home Control Using Body Gestures(2006)18 cited
- → An Intelligent Smart Home Control Using Body Gestures(2006)16 cited
- → Real‐time hand gestures system based on leap motion(2018)11 cited
- → Self-adjusting Bootstrapping(2011)
- Real-Time Gesture Recognition for Dynamic Applications(2015)