Strokes affect thousands of people worldwide leaving sufferers with severe disabilities affecting their daily activities. In recent years, new rehabilitation techniques have emerged such as constraint-induced therapy, biofeedback therapy and robot-aided therapy. In particular, robotic techniques allow precise recording of movements and application of forces to the affected limb, making it a valuable tool for motor rehabilitation. In addition, robot-aided therapy can utilise visual cues conveyed on a computer screen to convert repetitive movement practice into an engaging task such as a game. Visual cues can also be used to control the information sent to the patient about exercise performance and to potentially address psychosomatic variables influencing therapy. This paper overviews the current state-of-the-art on upper limb robot-mediated therapy with a focal point on the technical requirements of robotic therapy devices leading to the development of upper limb rehabilitation techniques that facilitate reach-to-touch, fine motor control, whole-arm movements and promote rehabilitation beyond hospital stay. The reviewed literature suggest that while there is evidence supporting the use of this technology to reduce functional impairment, besides the technological push, the challenge ahead lies on provision of effective assessment of outcome and modalities that have a stronger impact transferring functional gains into functional independence.

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This paper proposes an augmented reality based exoskeleton for virtual object assembly training. This proposed hand exoskeleton consists of 9 DOF joints which can provide force feedback to all 5 fingers at the same time. This device has ability to simulate shape, size, and weight of the virtual objects. In this augmented reality system, user can assembly virtual objects in real workspace which is superimposed with computer graphics information. During virtual object assembly training, user can receive force feedback which is synchronized with physics simulation. Since this proposed system can provide both visual and kinesthesia senses, it will help the users to improve their assembly skills effectively.

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Safety is an important factor for human-machine interface devices. Brake actuated devices are potentially safer than those that rely on motors for force-feedback generation. However, manipulators using conventional frictional brakes do have limitations. This paper presents the experimental evaluation of a newly developed four-state rotary programmable brake in a 2DOF manipulator. The experimental results show improved performance compared to the results obtained when manipulators with conventional frictional brakes are used.

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Experiments for the online closed-loop control of neural prosthetics require feedback within 100ms. In a typical neurophysiology laboratory with local computing machines, a majority of this time is spent on acquiring and analyzing the neural signals and a minority (i.e. less than a millisecond) is actual data transfer among machines on local- or campus-area networks. However, the local computing machines may not offer the computational resources necessary for running complex algorithms or scenarios that have been recently proposed. While scientists can take advantage of remote computing resource providers, wide-area networks present much larger latencies that can affect an online experiment. This work presents a split modeling approach that allows the execution of a controller on the neurophysiology resource and the execution of computationally intensive modeling and adaptation algorithms on a remote datacenter, even with the inevitable network latency. Simulation results are presented to quantify how the accuracy of the controller is affected by the split modeling approach in the presence of delays, and to demonstrate that scientists can take advantage of remotely available massive resources.

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Patients with neurological disorders, such as stroke survivors, can be treated with physical rehabilitation to regain motor control and function. Conventional therapy techniques are labor intensive and non-standardized. This is especially true in gait rehabilitation. A natural and tunable rehabilitation gait system (NaTUre_gaits) has been developed to assist gait rehabilitation with body-weight support locomotion training (BWSLT), gait control (GC), balance control (BC) and pelvic control (PC). All of those components are attached to a mobile platform (MP) with a total weight of more than 200kg. In order to coordinate the system with the motion of GC/BC/PC and to provide BWS for the subject throughout the walking test, the mobile system needs to move adaptively and steadily against those dynamic disturbance and heavy load. Motivated by reducing the tuning time, an identification and control method are proposed. Based on the relay response during operation, the system model is obtained and then PD controller with feed forward feedback is designed. Trials with/without subject are performed to demonstrate the effectiveness and merit of the proposed modeling and tuning method.

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