Selection performance based on classes of bimanual actions

We evaluated four selection techniques for volumetric data based on the four classes of bimanual action: symmetric-synchronous, asymmetric-synchronous, symmetric-asynchronous, and asymmetric-asynchronous. The purpose of this study was to determine the relative performance characteristics of each of these classes. In addition, we compared two types of data representations to determine whether these selection techniques were suitable for interaction in different environments. The techniques were evaluated in terms of accuracy, completion times, TLX overall workload, TLX physical demand, and TLX cognitive demand. Our results suggest that symmetric and synchronous selection strategies both contribute to faster task completion. Our results also indicate that no class of bimanual selection was a significant contributor to reducing or increasing physical demand, while asynchronous action significantly increased cognitive demand in asymmetric techniques and decreased ease of use in symmetric techniques. However, for users with greater computer usage experience, accuracy performance differences diminished between the classes of bimanual action. No significant differences were found between the two types of data representations.