Current changes in aging demographics poses new challenges: people require to keep their quality of life even after circumstances that threatened their movement and function. This increases the demand for new physical rehabilitation facilities that go beyond the traditional patient-therapist, one-to-one rehabilitation sessions. Two promising solutions rely on virtual reality and on the development of autonomous active orthoses, or exoskeletons. Whatever is the chosen approach, there is a requirement for a robust human-machine interface for the control, able to understand patient’s intention and to produce an immediate activation of the device. This paper presents a biomechanical model, a possible solution able to predict joint torque from the surface electromyography signals emitted by muscles during their activation. The main objective of the research is to investigate the benefits and efficacy of this model and to lay down the basis of our current research, whose main goal is to make possible a rehabilitation process either with active orthoses or virtual reality. Experiments involving all the steps of our model demonstrate the viability and effectiveness of our approach.

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Background: Current neuroscience has identified rehabilitation approaches with the potential to stimulate adaptive changes in the brains of persons with hemiparesis. These approaches include, intensive task-oriented training, bimanual activities and balancing proximal and distal upper extremity interventions to reduce competition between these segments for neural territory. Methods: This paper describes the design and feasibility testing of a robotic/virtual environment system designed to train the hand and arm of persons with hemiparesis. The system employs a simulated piano that presents visual, auditory and tactile feedback comparable to an actual piano. Arm tracking allows patients to train both the arm and hand as a coordinated unit, emphasizing the integration of both transport and manipulation phases. The piano trainer includes songs and scales that can be performed with one or both hands. Adaptable haptic assistance is available for more involved subjects. An algorithm adjusts task difficulty in proportion to subject performance. A proof of concept study was performed on four subjects with upper extremity hemiparesis secondary to chronic stroke to establish: a) the safety and feasibility of this system and b) the concurrent validity of robotically measured kinematic and performance measures to behavioral measures of upper extremity function. Results: None of the subjects experienced adverse events or responses during or after training. As a group, the subjects improved in both performance time and key press accuracy. Three of the four subjects demonstrated improvements in fractionation, the ability to move each finger individually. Two subjects improved their aggregate time on the Jebsen Test of Hand Function and three of the four subjects improved in Wolf Motor Function Test aggre-gate time. Conclusion: The system designed in this paper has proven to be safe and feasible for the training of hand function for persons with hemiparesis. It features a flexible design that allows for the use and further study of adjustments in point of view, bilateral and unimanual treatment modes, adaptive training algorithms and haptically rendered collisions in the context of reha-bilitation of the hemiparetic hand.

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(EN)Load measuring sections (50, 52) are provided with reaction sensors (50a, 50b, 52a, 52b) for measuring the loads at two positions, i.e. the tiptoes and the heels, on the sole side of the right and left feet of a person (12) wearing the device. The reaction sensor (50a) detects the reaction to a load on the front side of the right foot (right tiptoe), and the reaction sensor (50b) detects the reaction to a load on the rear side of the right foot (right heel). The reaction sensor (52a) detects the reaction to a load on the front side of the left foot (left tiptoe), and the reaction sensor (52b) detects the reaction to a load on the rear side of the left foot (left heel). Each reaction sensor (50a, 50b, 52a, 52b) is arranged to detect, based on the variation in capacitance, the loads acting on the right and left feet by the walking motion of the person (12) wearing the device, and can detect the variation in load incident to movement of weight and whether or not the foot of the person (12) wearing the device contacts the ground surface.
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In this paper, we present the dynamics, control, and preliminary experiments on a wearable upper arm exoskeleton intended for human users with four degrees-of-freedom (dof), driven by six cables. The control of this cable-driven exoskeleton is complicated because the cables can transmit forces to the arm only under tension. The standard PD controllers or computed torque controllers perform only moderately since the cables need to be in tension. Future efforts will seek to refine these control strategies and their implementations to improve functionality of a human user.

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Design of a flapping mechanism for flapping wing micro air vehicles (FWMAV) is presented based on a mathematical model of insect thorax. This model also includes an aerodynamic model of flapping wings. Using experiments on dynamically scaled wings and numerical optimization, the mechanism is tuned for peak aerodynamic performance. The thorax model is used to understand the mechanics of the biological flapping mechanism and reveals the significance of rotational stiffness and inertia distribution in flapping wings. Experiments conducted on the actual thorax based mechanism validate theoretical findings and also show significant lift generation capability.

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