Background: Few research in multiple sclerosis (MS) has focused on physical rehabilitation of upper limb dysfunction, though the latter strongly influences independent performance of activities of daily living. Upper limb rehabilitation technology could hold promise for complementing traditional MS therapy. Consequently, this pilot study aimed to examine the feasibility of an 8-week mechanical-assisted training program for improving upper limb muscle strength and functional capacity in MS patients with evident paresis. Methods: A case series was applied, with provision of a training program (3×/week, 30 minutes/session), supplementary on the customary maintaining care, by employing a gravity-supporting exoskeleton apparatus (Armeo Spring). Ten high-level disability MS patients (Expanded Disability Status Scale 7.0-8.5) actively performed task-oriented movements in a virtual real-life-like learning environment with the affected upper limb. Tests were administered before and after training, and at 2-month follow-up. Muscle strength was determined through the Motricity Index and Jamar hand-held dynamometer. Functional capacity was assessed using the TEMPA, Action Research Arm Test (ARAT) and 9-Hole Peg Test (9HPT). Results: Muscle strength did not change significantly. Signi-ficant gains were particularly found in functional capacity tests. After training completion, TEMPA scores improved (p = 0.02), while a trend towards significance was found for the 9HPT (p = 0.05). At follow-up, the TEMPA as well as ARAT showed greater impro-vement relative to baseline than after the 8-week intervention period (p = 0.01, p = 0.02 respectively). Conclusions: The results of present pilot study suggest that upper limb functionality of high-level disability MS patients can be positively influenced by means of a technology-enhanced physical rehabilitation program

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In recent years, the potential of robot-mediated upper extremity therapy has been widely acknowledged in stroke rehabilitation. The primary focus of rehabilitation robotics has been on building exoskeleton robots as well as devising control strategies that leverage robotic forces to adaptively assist and resist the movements of patients’ impaired arms during repetitive reach-touch exercises. In our single subject case study, a robot presents exercise target positions that are successively more difficult to the patient. This serves to adaptively challenge the patient, while inducing voluntary reach-touch movements. Through data collected during 12 weeks of therapy with a single subject, we demonstrate that a general purpose robot can induce desired therapeutic exercise movements from a patient. In addition, we have observed that the challenge level can be adapted as the patient improves his motor function, which leads to observable improvements in the motor function of the patient.

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This paper introduces a sensor suite framework for the partial automation of prosthetic arm control allowing high level control with a reduction of cognitive burden placed upon the user. Automation aims to replicate the hand eye co-ordination through the synergy of a virtual 7DOF arm prosthesis together with the development of a gaze tracking system. The interactions between elements of the suite are detailed and a selection of sensors implemented to control a simple simulation. Use of the novel tongue control system is used to provide discrete input to the system. Initial tests are made of of the system together with a users ability to learn to use the system with promising user feedback on ease of interaction and potential for reduced cognitive burden.

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This paper describes the preliminary development of a haptic setup for capturing and simulating musculoskeletal assessment and manipulation of the hand. A haptic device, called MasterFinger-2, is used for capturing one massage technique and one joint manipulation technique, and also for simulating this manipulation technique that can be used in both assessment and treatment of the hand. First, works developed demonstrate that an application of haptic devices enable quantitative characterization of forces and positions used in manipulation of musculoskeletal structures. Second, an application for simulation is developed using the MasterFinger-2 to display (both visually and haptically) manipulations of one joint of the hand around three axes. The novel aspects of this approach are the use of a multifinger device for capture, simulation, and modeling the movement of a biological joint for haptic simulation across three axes, each with nonlinear behavior.

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• Evolutionary Computing (EC): Search algorithms
based on the mechanisms of natural selection and natural genetics (survival of the fittest).
• Machine Learning (ML): About making computers modify or adapt their actions so that these actions get more accurate, where accuracy is measured by how well the chosen actions reflect the correct ones.

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