This paper introduces a novel exoskeleton device (HANDEXOS) for the rehabilitation of the hand for post-stroke patients. The nature of the impaired hand can be summarized in a limited extension, abduction and adduction leaving the fingers in a flexed position, so the exoskeleton goal is to train a safe extension motion from the typical closed position of the impaired hand. The mechanical design of HANDEXOS offers the possibility to overcome the exoskeleton limits often related to the general high level of complexity of the structure, mechanism and actuation. We describe the mechanical design of the index finger module, the dynamic model and some preliminary experimental results.

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EMG-to-force estimation for voluntary muscle contraction has many applications in human-machine interaction, motion analysis, and rehabilitation robotics for prosthetic limbs or exoskeletons. EMG-based model can account for a subject’s individual activation patterns to estimate muscle force. For the estimation, so-called Hill-type model has been used in most of the cases. It already has shown its promising performance, but it is still known as a phenomenological model considering only macroscopic physiology. We have already developed the physiological based muscle model for the use of functional electrical stimulation (FES) which can render the myoelectrical property also in microscopic scale. In this paper we discuss EMG-to-force estimation based on this full physiological based muscle model in voluntary contraction. In addition to Hill macroscopic structure, a microscopic physiology originally designed by Huxley is integrated. It has significant meaning to realize the same kind of EMG-to-force estimation with a physiological based model not with a phenomenological model, because it brings the understanding of the internal biophysical dynamics and new insights about neuromuscular activations. Using same EMG data of isometric muscle contraction, the force estimation results are shown by classical approach and new physiological based approach. Its interpretation is also discussed.

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Разработана система преобразования мозговых волн в радиосигналы с FM-модуляцией и последующего декодирования их в виде звука. Проект, согласно утверждению создателей, является первым полностью беспроводным интерфейсом связи между компьютером и мозгом. Пока возможно лишь воспроизведение гласных звуков, и хотя это не соответствует уровню проводных решений, работа над перспективной технологией продолжается.

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This paper is focused on the use of Zero Moment Point (ZMP) concept for balancing control of the Asian Institute of Technology Leg EXoskeleton-I (ALEX-I). ALEX-I has been developed to assist patients who suffer from paraplegia or immobility due to the loss of power on lower limbs. The balanced posture set-points (joint trajectories) under ZMP criterion are generated offline. The ZMP based set points are provided as the desired postures to ALEX-I. Fuzzy Logic Controller (FLC) determi-nes the modified set points based on postures balancing sensed by loadcells. Ground Contact Point (GCP) is used to find the “ZMP-like in real time”. GCP data is obtained by placing 4 loadcells forming a force plate on each foot of ALEX-I. This GCP data is then compared with the reference ZMP. Uncertainties of the model parameters, backlash, and joint tolerance are considered as disturbance. The differences of ZMP and GCP on x-z plane are used as the inputs to the FLC. The 4 outputs from FLC are the compensated angles of left and right ankles joints in roll and pitch axes that make the actual ZMP locate in the convex hull of the supporting area.

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Neural stimulation devices are described that detect the neural activity from the spinal cord in a semi-invasive manner, where the device comprises at least one antenna array comprising an antenna. The antenna of the array is in electrical communication with the spinal cord of the patient. A device comprising more than one antenna array can be used to detect the neural signal strength, as well as the velocity and directionality of the signal.

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