Extending Fitts' law in three-dimensional virtual environments with current low-cost virtual reality technology. Issue 139 (July 2020)

• Record Type: Journal Article
• Authors: Clark, Logan D.; Bhagat, Aakash B.; Riggs, Sara L.
• Is Part Of: International journal of human-computer studies. Issue 139(2020)
• Publication Date: 2020-07
• Subjects: Fitts' law -- Virtual reality -- Oculus Rift -- Depth perception -- Stereoscopic display; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2020.102413
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: Fitts' law predicts movement time (MT) for 3D discrete pointing movements in VR. Target size, movement distance, and inclination angle affect MT. Target size modulates the effect of inclination angle on MT in virtual reality. Including target depth can improve the predictive power of Fitts' law models in VR. Abstract: Virtual reality (VR) interfaces require users to perform three-dimensional reaching and pointing movements to interact with objects positioned within the user's arm's reach. However, there has been limited work that has evaluated the applicability of established models of human motor control to model performance of these tasks in 3D virtual reality environments using current low-cost technologies. In this study, a 3D discrete pointing task using the Oculus Rift system was used to explore potential influences on movement in VR and to account for these influences in a new formulation of Fitts' law. Target size and distance from the starting point of movement were systematically varied to generate a broad range of index of difficulty (ID) values. Target locations were specified using a spherical coordinate system in which inclination angle corresponded to the pitch of the movement axis with respect to the starting point of movements and azimuth angle corresponded to the roll of the movement axis with respect to the horizontal plane. In line with previous work, we observed that target size, radial distance, and inclination angle had a significant effect on movement time. The effect of inclination angle varied with target size, which suggests that target size affected depth estimation. Significant target characteristics and interaction effects were used to develop an extended Fitts' law model, which accounted for 64.5% of the variation in movement times. Comparisons to other Fitts' law models revealed that models accounting for the effects of target depth improved predictive power relative to the traditional Fitts' law formulation. Together, these findings support the value of extending Fitts' law models to account for domain-specific constraints in VR environments. We discuss these results in the context of previous work examining HMD display deficiencies and discrete 3D pointing tasks, and suggest several directions for future work.