Intensified rehabilitation is important for stroke survivors but difficult to achieve due to limited access to physiotherapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based physiotherapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, 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 effectively, 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 effective, with important lessons learned which will inform future design.
|Title of host publication||Unknown Host Publication|
|Publisher||University of Reading|
|Number of pages||9|
|Publication status||Published online - 20 Sept 2016|
|Event||11th International Conference on Disability, Virtual Reality & Associated Technologies - |
Duration: 20 Sept 2016 → …
|Conference||11th International Conference on Disability, Virtual Reality & Associated Technologies|
|Period||20/09/16 → …|
- Virtual Reality
- Upper arm
- stroke Rehabilitation
- Leap Motion
- Oculus Rift.