Designing and Analyzing In-Place Motor Tasks in Virtual Reality With Goal Functions.

Goal functions make virtual goal-oriented motor tasks easier to analyze and manipulate by explicitly linking movement to outcome. However, they have only been used to study constrained (e.g., planar) upper limb movements. We present a design framework for integrating goal functions with unconstrained postural and upper limb movements in a virtual reality (VR) device. VR tasks designed with the framework can mimic unconstrained natural motions and thus train a range of functional movements yet remain analytically tractable. We created three in-place VR motor tasks: a bow-and-arrow, a reach-and-strike, and a punching bag task. Each task was adjusted to subject-specific workspace limits and anthropometrics. We studied the effects of 3 days of practice and 3 reach/lean distances on task performance in 12 healthy adults. Subjects performed all tasks on day 1 with moderate proficiency and improved with practice at all reach/lean distances. Task-specific results showed that performance decreased and movement variability increased near the edge of the reaching workspace; viewing angles and the imperfect depth cues in VR likely led to biases in performance and practice could attenuate the former effect; in reach-and-strike, subjects learned movement patterns similar to those seen in a real-world striking sport. These results show that our framework can deliver tasks useful for analyzing and training motor performance and can guide future in-place motor training. Post-hoc, we demonstrated the feasibility of generalizable methods that adjust required movement speeds and task difficulty for impaired populations.