Integrated arm and hand training using adaptive robotics and virtual reality simulations
A S Merians, G G Fluet, Q Qiu, S Saleh, I Lafond, S V Adamovich
University of Medicine and Dentistry of New Jersey, USA
Virtual Reality simulations interfaced with robotic arm devices are being used for training the upper extremity of people post-stroke. The benefit has been hypothesized to be the ability to provide repetitive task practice, directed visual and auditory feedback, learning algorithms and graded resistive and assistive forces. All of these elements can be manipulated to provide individualized motor learning paradigms. We have developed a unique exercise system, interfaced with complex virtual reality gaming simulations that can train both the upper arm and the hand of people in the chronic phase post-stroke. After two weeks of intensive training, eleven subjects, were able to more effectively control the limb during hand interaction with the target as demonstrated by improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of fractionation and improved timing. These changes in kinematic measures were accompanied by robust changes in functional tests of upper extremity motor control, the Wolf Motor Function Test, the Jebsen Test of Hand Function and the 9-hole Peg Test.
Alma Merians (lead author, pictured) ICDVRAT Conference Website