This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patient's intention is decoded by means of an active vision system, through the combination of a Kinect-based vision system, which can online robustly identify and track 3-D objects, and an eye-tracking system for objects selection. At the level of action generation, a BCI is used to control the patient's intention to move his/her own arm, on the basis of brain activity analyzed during motor imagery. The main kinematic parameters of the reaching movement (i.e., speed, acceleration, and jerk) assisted by the robot are modulated by the output of the BCI classifier so that the robot-assisted movement is performed under a continuous control of patient's brain activity. The system was experimentally evaluated in a group of three healthy volunteers and four chronic stroke patients. Experimental results show that all subjects were able to operate the exoskeleton movement by BCI with a classification error rate of 89.4±5.0% in the robot-assisted condition, with no difference of the performance observed in stroke patients compared with healthy subjects. This indicates the high potential of the proposed gaze-BCI-driven robotic assistance for neurorehabilitation of patients with motor impairments after stroke since the earliest phase of recover
This paper proposes an augmented reality visualization interface to simultaneously present visual and laser sensors information further enhanced by stereoscopic viewing and 3-D graphics. The use of graphic elements is proposed to represent laser measurements that are aligned to video information in 3-D space. This methodology enables an operator to intuitively comprehend scene layout and proximity information and so to respond in an accurate and timely manner. The use of graphic elements to assist teleoperation, sometime discussed in the literature, is here proposed following an innovative approach that aligns virtual and real objects in 3-D space and color them suitably to facilitate comprehension of objects proximity during navigation. This paper is developed based on authors' previous experience on stereoscopic teleoperation. The approach is experimented on a real telerobotic system, where a user operates a mobile robot located several kilometers apart. The result showed simplicity and effectiveness of the proposed approach
During typical robot-assisted training sessions, patients are required to execute tasks with the assistance of a robot while receiving feedback on a 2D display. Three-dimensional tasks of this sort require the adoption of stereoscopy to achieve correct visuo-motor-proprioceptive alignment. Stereoscopy often causes side-effects as sickness and tiredness, and it may affect the processes of recovery and cortical reorganization of the patients' brain in an unclear way. It follows that it is preferrable for a robot-assisted neurorehabilitation therapy to work in a real 3D setup containing real objects rather than using virtual reality.In this paper, we propose a new system for robot-assisted neurorehabilitation scenarios which allows patients to execute therapy by manipulating real, generic 3D objects. The proposed system is based on a new algorithm for identification and tracking of generic objects which makes efficient use of a Microsoft Kinect sensor. We discuss the results of several experiments conducted in order to test robustness, accuracy and speed of the tracking algorithm and the feasibility of the integrated system.
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