LenSelect: Object Selection in Virtual Environments by Dynamic Object Scaling

AbstractWe present a novel selection technique for VR called LenSelect. The main idea is to decrease the Index of Difficulty (ID) according to Fitts’ Law by dynamically increasing the size of the potentially selectable objects. This facilitates the selection process especially in cases of small, distant or partly occluded objects, but also for moving targets. In order to evaluate our method, we have defined a set of test scenarios that covers a broad range of use cases, in contrast to often used simpler scenes. Our test scenarios include practically relevant scenarios with realistic objects but also synthetic scenes, all of which are available for download. We have evaluated our method in a user study and compared the results to two state-of-the-art selection techniques and the standard ray-based selection. Our results show that LenSelect performs similar to the fastest method, which is ray-based selection, while significantly reducing the error rate by 44%

Improving expressivity in desktop interactions with a pressure-augmented mouse

Desktop-based Windows, Icons, Menus and Pointers (WIMP) interfaces have changed very little in the last 30 years, and are still limited by a lack of powerful and expressive input devices and interactions. In order to make desktop interactions more expressive and controllable, expressive input mechanisms like pressure input must be made available to desktop users. One way to provide pressure input to these users is through a pressure-augmented computer mouse; however, before pressure-augmented mice can be developed, design information must be provided to mouse developers. The problem we address in this thesis is that there is a lack of ergonomics and performance information for the design of pressure-augmented mice. Our solution was to provide empirical performance and ergonomics information for pressure-augmented mice by performing five experiments. With the results of our experiments we were able to identify the optimal design parameters for pressure-augmented mice and provide a set of recommendations for future pressure-augmented mouse designs