Current brain–computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the stan-dard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min−1 and of 26.56 ± 9.224 bit min−1, respectively. A paired t-test failed to reveal significant dif-ferences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI
applications.

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There are many robotic arms developed for providing care to physically disabled people. It is difficult to find robot designs in literature that articulate such a procedure. Therefore, it is our hope that the design work shown in this study may serve as a good example of a systematic method for rehabilitation robot design. Approach: The arm exoskeleton suit was developed to increase human’s strength, endurance, or speed enabling them to perform tasks that they previously could not perform. It should not impede the user’s natural motion and should be comfortable and safe to wear and easy to use. Although movement is difficult for them, they usually want to go somewhere by themselves. Results: The kinematic exoskeleton suit for human arms is simulated by MATLAB software. The exoskeleton suit of human arm consists of one link length, three link twists, two link offsets and three joint angles. Conclusion: This study introduced the kinematic of exoskeleton suit for human arm. The exoskeleton suit can be used to be instrument for anyone who needs to improve human’s performance. It will increase the strength of human that can lift heavy load or help handicapped patients, who cannot use their arm.

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This paper deals with a comparative study on using water and air as actuation means for the control of a fluidic muscle (designed for air) and assesses the performance, parti-cularly from a dynamic and energetic point of view. A medium with higher bulk modulus such as oil/water is believed to increase pressure and force bandwidths and reduce sensitivity to load variations, as is the case with conventional hydraulic stiff actuation systems. However in this application the inherent flexibility of the muscle plays a major role. Water has been chosen because of its non-flammability, environmental friendliness and the low solubility of air in it. The operating pressure range of the pneumatic muscle is 0-6 bar (typical range of a pneumatic system) that is well below typical perating pressures of hydraulic systems (typically over 100 bar). At such low pressures the dynamic behaviour of water is less predictable because of the higher likelihood of entrapped air in the water which physically occurs when operating at low pressures. This can majorly affect water bulk modulus and hence its dynamic performance. Therefore, the behaviour of the system in this unconventional pressure range for a liquid must be more thoroughly investigated. Theoretical and experimental analyses on a dedicated test rig have been carried out to assess these assumptions.

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Patterns of stereotyped muscle coactivation, clinical-ly referred to as synergies, emerge following stroke and impair arm function. Although researchers have fo-cused on cortical contributions, there is growing evidence that altered stretch reflex pathways may also contribute to impairment. However, most previous reflex studies have focused on passive, single-joint move-ments without regard to their coordination during volitional actions. The purpose of this study was to exa-mine the effects of stroke on coordinated activity of stretch reflexes elicited in multiple arm muscles following multijoint perturbations. We hypothesized that cortical injury results in increased stretch ref-lexes of muscles characteristic of the abnormal flexor synergy during active arm conditions. To test this hypothesis, we used a robot to apply position perturbations to impaired arms of 10 stroke survivors and dominant arms of 8 healthy age-matched controls. Corresponding reflexes were assessed during volitional contractions simulating different levels of gravitational support, as well as during voluntary flexion and extension of the elbow and shoulder. Reflexes were quantified by average rectified surface electromyogram, recorded from eight muscles spanning the elbow and shoulder. Reflex coordination was quantified using an independent components analysis. We found stretch reflexes elicited in the stroke group were significantly less sensitive to changes in background muscle activation compared with those in the control group (P 0.05). We also observed significantly increased reflex coupling between elbow flexor and shoulder abduc-tor– extensor muscles in stroke subjects relative to that in control subjects. This increased coupling was present only during volitional tasks that required elbow flexion (P
0.001), shoulder extension (P 0.01), and gravity opposition (P
0.01), but not during the “no load” condition. During volitional contractions, reflex amplitudes scaled with the level of impairment, as assessed by Fugl-Meyer scores (r2  0.63; P 0.05). We conclude that altered reflex coordination is indicative of motor impairment level and may contri-bute to impaired arm function following stroke.

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The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient’s motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phase-dependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMG-based exoskeleton for rehabilitation purposes.

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