Recent neural science research suggests that a
robotic device can be an effective tool to deliver the repetitive
movement training that is needed to trigger neuroplasticity
in the brain following neurologic injuries such as stroke
and spinal cord injury (SCI). In such scenario, adaptive
control of the robotic device to provide assistance as needed
along the intended motion trajectory with exact amount of
force intensity, though complex, is a more effective approach.
A critic-actor based reinforcement learning neural network
(RLNN) control method is explored to provide adaptive control
during post-stroke fine hand motion rehabilitation training.
The effectiveness of the method is verified through computer
simulation and implementation on a hand rehabilitation robotic
device. Results suggest that the control system can fulfil the
assist-as-needed (AAN) control with high performance and
reliability. The method demonstrates potential to encourage
active participation of the patient in the rehabilitation process
and to improve the efficiency of the process.