Search

Intensive rehabilitation is important for stroke survivors but difficult to achieve due to limited access to rehabilitation therapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based rehabilitation therapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, namely the Microsoft Kinect, Leap Motion and Myo armband, to track upper arm and body motion. Linear regression was applied to 3D user motion data using four popular forms of Fitts's law and each approach evaluated. While all four forms of Fitt's Law produced similar results and could model users adequately, it may be argued that a 3D tailored form provided the best fit. However, we propose that Fitts's Law may be more suitable as the basis of a more complex model to profile user performance. Evaluated by healthy users TAGER proved robust, with valuable lessons learned to inform future design. The majority of users enjoyed using the Leap Motion controller and VR Headset, the inclusion of visual cues shows a general improvement in target acquiring performance and that Fitts's Law can be used to linearly model user reaching and pointing movements in 3D environments. However, in some situations this isn't the case, emphasizing the importance of user profiling to examine the user's kinematic behavior more intricately. [ABSTRACT FROM AUTHOR]