Robot-aided neurorehabilitation in sub-acute and chronic stroke: Does spontaneous recovery have a limited impact on outcome?

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evaluation of upper limb sense of position in healthy individuals and patients after stroke

The aims of this study were to develop and evaluate reliability of a quantitative assessment tool for upper limb sense of position on the horizontal plane. We evaluated 15 healthy individuals (controls) and 9 stroke patients. A robotic device passively moved one arm of the blindfolded participant who had to actively move his/her opposite hand to the mirror location in the workspace. Upper-limb's position was evaluated by a digital camera. The position of the passive hand was compared with the active hand's 'mirror' position. Performance metrics were then computed to measure the mean absolute errors, error variability, spatial contraction/expansion, and systematic shifts. No significant differences were observed between dominant and non-dominant active arms of controls. All performance parameters of the post-stroke group differed significantly from those of controls. This tool can provide a quantitative measure of upper limb sense of position, therefore allowing detection of changes due to rehabilitation

Tracking Motor Improvement at Subtask Level DuringRobot-Aided Neurorehabilitation of Stroke Patients

Background. Robot-aided neurorehabilitation can provide intensive, repetitious training to improve upper limb function after stroke. To be more effective, motor therapy ought to be progressive and continuously challenge the patient’s ability. Current robotic systems have limited customization capability and require a physiotherapist to assess progress and adapt therapy accordingly. Objective. We aimed to track motor improvement during robot-assistive training and test a tool to more automatically adjust training. Methods. Eighteen participants with chronic stroke were trained using a multi-component reaching task assisted by a shoulder-elbow robotic assist. The time course of motor gains was assessed for each subtask of the practiced exercise. A statistical algorithm was then tested on simulated data to validate its ability to track improvement and subsequently applied to the recorded data to determine its performance compared to a therapist. Results. Patients' recovery of motor function exhibited a time course dependent on the particular component of the executed task, suggesting that differential training on a subtask level is needed to continuously challenge the neuromuscular system and boost recovery. The proposed algorithm was tested on simulated data and was proven to track overall patient's progress during rehabilitation. Conclusions. Tuning of the training program at subtask level may accelerate the process of motor relearning. The algorithm proposed to adjust task difficulty opens new possibilities to automatically customize robotic-assistive training

Evaluation of upper limb sense of position in healthy individuals and patients after stroke.

The aims of this study were to develop and evaluate reliability of a quantitative assessment tool for upper limb sense of position on the horizontal plane. We evaluated 15 healthy individuals (controls) and 9 stroke patients. A robotic device passively moved one arm of the blindfolded participant who had to actively move his/her opposite hand to the mirror location in the workspace. Upper-limb's position was evaluated by a digital camera. The position of the passive hand was compared with the active hand's 'mirror' position. Performance metrics were then computed to measure the mean absolute errors, error variability, spatial contraction/expansion, and systematic shifts. No significant differences were observed between dominant and non-dominant active arms of controls. All performance parameters of the post-stroke group differed significantly from those of controls. This tool can provide a quantitative measure of upper limb sense of position, therefore allowing detection of changes due to rehabilitation

Robot Therapy for Severely Impaired Stroke Survivors: Toward a Concurrent Regulation of Task Difficulty and Degree of Assistance

Many exercise protocols for robot therapy are designed to adjust their degree of difficulty in order to maintain a constant challenge level. A simple way to do this is to design exercises that consist of a variable number of sub-movements in different directions - task difficulty is determined by the number of sub-movements. But, how does recovery proceed in these tasks, and how to regulate the magnitude of the assistance provided by the robot in this case? Here we focus on a simple task in which subjects had to complete a square figure. At every trial, an adaptive regulator selects the appropriate degree of robot assistance needed to complete the entire figure. We tested this protocol with four severely impaired stroke survivors during a multisession study. Robotic training succeeded - the controller gradually reduced the degree of assistance while performance remained constant, suggesting that in fact recovery took place. We used a dynamic model of the recovery process to further analyze the effects of the assistive force and the temporal evolution of the subjects' voluntary control. The model provided an excellent fitting of the subjects' performance and revealed that magnitude and modalities of recovery are very different in the different sub-movements. These results suggest that in order to maximize the recovery the modulation of assistance should occur at the level of each sub-movement