Error augmentation enhancing arm recovery in individuals with chronic stroke: a randomized crossover design.

Background: Neurorehabilitation studies suggest that manipulation of error signals during practice can stimulate improvement in coordination after stroke.

Objective: To test visual display and robotic technology that delivers augmented error signals during training, in participants with stroke.

Methods: A total of 26 participants with chronic hemiparesis were trained with haptic (via robot-rendered forces) and graphic (via a virtual environment) distortions to amplify upper-extremity (UE) tracking error.

Arm control recovery enhanced by error augmentation.

Here we present results where nineteen stroke survivors with chronic hemiparesis simultaneously employed the trio of patient, therapist, and machine. Massed practice combined with error augmentation, where haptic (robotic forces) and graphic (visual display) distortions are used to enhance the feedback of error, was compared to massed practice alone. The 6-week randomized crossover design involved approximately 60 minutes of daily treatment three times per week for two weeks, followed by one week of rest, and then repeated using the alternate treatment protocol.

Use of an electromyographically driven hand orthosis for training after stroke.

A pilot study was conducted to test the feasibility of using electromyographic signals to drive an active orthosis for hand therapy after stroke. Five stroke survivors with chronic hemiparesis completed 18 one-hour training sessions over 6 weeks. Activation patterns of a long finger flexor muscle and a long finger extensor muscle controlled an orthosis, the J-Glove, which provided assistance to finger extension to facilitate grasp-and-release movements.

Haptic/graphic rehabilitation: integrating a robot into a virtual environment library and applying it to stroke therapy.

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other forms of biofeedback. Previous human-robot interactive systems have required different software commands to be implemented for each robot leading to unnecessary developmental overhead time each time a new system becomes available. For example, when a haptic/graphic virtual reality environment has been coded for one specific robot to provide haptic feedback, that specific robot would not be able to be traded for another robot without recoding the program.