[ARTICLE] A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist – Full Text PDF

Abstract

Demand for robot-assisted therapy has increased at every stage of the neurorehabilitation recovery. This paper presents a controller that is suitable for the assist-as-needed (AAN) training of the wrist when performing the spatial motion. A compact wrist exoskeleton robot is presented to realize the AAN controller. This wrist robot includes series elastic actuators with high torque-to-weight ratios to provide accurate force control required for the AAN controller. In addition to assist-as-needed rehabilitation, the parameters of the AAN controller can be adjusted to deliver passive, active, or resistive rehabilitation. Experimental results demonstrate that the proposed AAN controller can provide the total solution to cover each stage of wrist spatial-motion rehabilitation.

(a) Orientation of the wrist and handlebar (b) Omni-directional stiffness K and omni-directional damping B

via (PDF) A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

[ARTICLE] Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot.

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.

Source: IEEE Xplore Abstract (Abstract) – Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitati…

[ARTICLE] A Subject-Adaptive Controller for Wrist Robotic Rehabilitation

…In order to derive maximum benefit from robot-assisted rehabilitation, it is critical that the implemented control algorithms promote the participant’s active engagement in therapy. Assist-as-needed (AAN) controllers address this need by providing only appropriate assistance during movement execution. Often, these controllers depend on the definition of an optimal movement profile, against which the participant’s movements are compared. In this paper, we present a novel subject-adaptive controller, consisting of two main components: AAN control algorithm and online trajectory recalculation…

via IEEE Xplore Abstract – A Subject-Adaptive Controller for Wrist Robotic Rehabilitation.