Background: Robot-assisted therapy offers a promising approach to neurorehabilitation, particularly for severely to moderately im-paired stroke patients. The objective of this study was to investigate the effects of intensive arm training on motor performance in four chronic stroke patients using the robot ARMin II. Methods: ARMin II is an exoskeleton robot with six degrees of freedom (DOF) moving shoulder, elbow and wrist joints. Four volunteers with chronic (≥ 12 months post-stroke) left side hemiparesis and dif-ferent levels of motor severity were enrolled in the study. They received robotassisted therapy over a period of eight weeks, three to four therapy sessions per week, each session of one hour. Patients 1 and 4 had four one-hour training sessions per week and patients 2 and 3 had three onehour training sessions per week. Primary outcome variable was the Fugl-Meyer Score of the upper extremity Assessment (FMA), secondary outcomes were the Wolf Motor Function Test (WMFT), the Catherine Bergego Scale (CBS), the Maximal Voluntary Torques (MVTs) and a questionnaire about ADL-tasks, progress, changes, motivation etc. Results: Three out of four patients showed significant improvements (p < 0.05) in the main outcome. The improvements in the FMA scores were aligned with the objective results of MVTs. Most improvements were maintained or even increased from discharge to the six-month follow-up. Conclusion: Data clearly indicate that intensive arm therapy with the robot ARMin II can significantly improve motor function of the paretic arm in some stroke patients, even those in a chronic state. The findings of the study provide a basis for a subsequent controlled randomized clinical trial. (далее…)

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The this work deals with neural network-based gait-pattern adaptation algorithms for an active lower limbs orthosis. Stable trajectories are generated during the optimization process, considering a stable trajectory generator based on the Zero Moment Point criterion and the inverse dynamic model. Additionally, two neural network (NN) are used to decrease the time-consuming computation of the model and ZMP optimization. The first neural network approximates the inverse dynamics and the ZMP optimization, while the second one works in the optimization procedure, giving the adapting parameter according to orthosis-patient interaction. Also, a robust controller based on the ¿_¿ method is designed to attenuate the effects of external disturbances and parametric uncertainties in the trajectory tracking errors. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results.

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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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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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