This study examines the feasibility of using electroencephalograms (EEGs) to rapidly detect the intent to open one’s hand in individuals with complete hand paralysis following a subcortical ischemic stroke. If detectable, this motor planning activity could be used in real time to trigger a motorized hand exoskeleton or an electrical stimulation device that opens/closes the hand. While EEG-triggered movement-assist devices could restore function, they may also promote recovery by reinfor-cing the use of remaining cortical circuits. EEGs were recorded while participants were cued to either relax or attempt to extend their fingers. Linear discriminant analysis was used to detect onset of finger extension from the EEGs in a leave-onetrial-out cross-validation process. In each testing trial, the classifier was applied in pseudo real time starting from an initial hand-relaxed phase, through movement planning, and into the initial attempted finger extension phase (finger extension phase estimated from typical time-to-movement-onset measured in the unaffected hand). The classifiers detected attempted finger-extension at a significantly higher rate during both motor planning and early attempted execution compared to rest.

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This paper investigates the control algorithm of an exoskeleton for hand rehabilitation, which accomplishes both active and passive rehabilitation training. In the passive mode control the PID control algorithm is executed in the velocity mode of the driver. In the active mode control, control architecture is proposed to deal with in both free space and constraint space. A resistance compensation control method is proposed to reduce the resistance in free space which is caused by the friction of the Bowden cable as well as the moment of inertial. To realize the compensation, force sensors are used to measure the force exerted by the human fingertip. A commercial driver, which could switch between the two control modes by a programmable digital switch rather than changing the physical connection manually, guarantees the realization of the required functions. The experiments are conducted to verify the proposed method, and the results show that in the active control mode, the maximum finger-exerted force with compensation is about two fifths of the force without compensation which means the resistance is greatly reduced. And in the passive mode, the maximum joint position error is about 1.2 degree, which satisfies the requirement in hand rehabilitation application. The experimental results demonstrate the validity of the proposed method.

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For the human hand rehabilitation, the palmar opposition is an important function to evaluate the recovery of hand motor capabilities. This paper proposes an exoskeleton-type hand rehabilitation assistive device which is able to be applied to index finger as well as thumb. The kinematics and workspace of index fingertip and thumb-tip are analyzed. The `opposition space’ is defined as the intersection of the workspace of thumb-tip and the index fingertip. A metric, what we call `opposition degree’, which is defined as the ratio of volume of opposition space to that of the index fingertip workspace, is proposed to present the available degree of opposition. As an example, the opposition degree of a human hand model is determined. This metric can also used to rate the ergonomic performance of the hand rehabilitation assistive device. This method can be applied to other fingers as well. The opposition simulation experiment is conducted to verify the kinematics of the proposed exoskeleton.

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This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user’s feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and develo-ping a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo — Spain, presenting great acceptability by spinal chord injury patients and clinical staff.

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Robotic systems that are interfaced with virtual reality gaming and task simulations are increasingly being developed to provide repetitive intensive practice to promote increased compliance and facilitate better outcomes in rehabilitation post-stroke. A major development in the use of virtual environments (VEs) has been to incorporate tactile information and interaction forces into what was previously an essentially visual experience. Robots of varying complexity are being interfaced with more traditional virtual presentations to provide haptic feedback that enriches the sensory experience and adds physical task parameters. This provides forces that produce biomechanical and neuromuscular inter-actions with the VE that approximate real-world movement more accurately than visual-only VEs, simulating the weight and force found in upper extremity tasks. The purpose of this article is to present an overview of several systems that are commercially available for ambulation training and for training movement of the upper extremity. We will also report on the system that we have developed (NJIT-RAVR system) that incorporates motivating and challenging haptic feedback effects into VE simulations to facilitate motor recovery of the upper extremity post-stroke. The NJIT-RAVR system trains both the upper arm and the hand. The robotic arm acts as an interface between the participants and the VEs, enabling multiplanar movements against gravity in a three-dimensional workspace. The ultimate question is whether this medium can provide a motivating, challenging, gaming experience with dramatically decreased physical difficulty levels, which would allow for participation by an obese person and facilitate greater adherence to exercise regimes.

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