In this paper, we explore the idea of designing nonanthropomorphic, multi-fingered robotic hands for tasks that replicate the motion of the human hand. Taking as input data rigid-body trajectories for the five fingertips, we develop a method to perform dimensional synthesis for a kinematic chain with a tree structure, with three common joints and five branches. We state the forward kinematics equations of relative displacements for each serial chain expressed as dual quaternions, and solve for up to five chains simultaneously to reach a number of poses along the hand trajectory using a hybrid global numerical solver that integrates a genetic algorithm and a Levenberg- Marquardt local optimizer. Although the number of candidate solutions in this problem is very high, the use of the genetic algorithm lets us to perform an exhaustive exploration of the solution space and retain a subset of them. We then can choose some of the solutions based on the specific task to perform. Note that these designs could match the task exactly while having a finger design radically different from that of the human hand.

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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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• 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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Background: Recovery of upper extremity function is particularly recalcitrant to successful rehabilitation. Roboticassisted arm training devices integrated with virtual targets or complex virtual reality gaming simulations are being developed to deal with this problem. Neural control mechanisms indicate that reaching and hand-object manipulation are interdependent, suggesting that training on tasks requiring coordinated effort of both the upper arm and hand may be a more effective method for improving recovery of real world function. However, most robotic therapies have focused on training the proximal, rather than distal effectors of the upper extremity. This paper describes the effects of robotically-assisted, integrated upper extremity training. Methods: Twelve subjects post-stroke were trained for eight days on four upper extremity gaming simulations using adaptive robots during 2-3 hour sessions. Results: The subjects demonstrated improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of finger individuation and improved speed. Importantly, these changes were accompanied by a robust 16-second decrease in overall time in the Wolf Motor Function Test and a 24-second decrease in the Jebsen Test of Hand Function. Conclusions: Complex gaming simulations interfaced with adaptive robots requiring integrated control of shoulder, elbow, forearm, wrist and finger movements appear to have a substantial effect on improving hemiparetic hand function. We believe that the magnitude of the changes and the stability of the patient’s function prior to training, along with maintenance of several aspects of the gains demonstrated at retention make a compelling argument for this approach to training.

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