Using relative head and hand-target features to predict intention in 3D moving-target selection

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Casallas, Juan
Merienne, Frederic
Garbaya, Samir
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Oliver, James
Kelly, Jonathan
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Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
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Virtual Reality Applications Center
At VRAC, our mission is clear: “To elevate the synergy between humans and complex interdisciplinary systems to unprecedented levels of performance”. Through our exceptional Human Computer Interaction (HCI) graduate program, we nurture the next generation of visionaries and leaders in the field, providing them with a comprehensive understanding of the intricate relationship between humans and technology. This empowers our students to create intuitive and transformative user experiences that bridge the gap between innovation and practical application.
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The Department of Psychology may prepare students with a liberal study, or for work in academia or professional education for law or health-services. Graduates will be able to apply the scientific method to human behavior and mental processes, as well as have ample knowledge of psychological theory and method.
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Abstract: Selection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ~ 72% accuracy on general moving-target selection tasks and up to ~ 78% by also including task-related target properties.


This is a manuscript of a conference proceeding published as Casallas, Juan Sebastian, James H. Oliver, Jonathan W. Kelly, Frédéric Merienne, and Samir Garbaya. "Using relative head and hand-target features to predict intention in 3D moving-target selection." In Virtual Reality (VR), 2014 iEEE, pp. 51-56. IEEE, 2014. Posted with permission.

Wed Jan 01 00:00:00 UTC 2014