(EN)Provided is an action-aiding system, in which the action state of the wearable type action-aiding device (10-1) of a doctor (1) is transmitted via a network to the wearable action-aiding device (10-2) of a patient (2). This action state is reflected on the wearable action-aiding device (10-2) of the patient (2) so that the rehabilitation of the patient (2) is performed. Moreover, the action state of the wearable action-aiding device (10-2) is transmitted from the wearable action-aiding device (10-2) of the patient (2) to the wearable type action-aiding device (10-1). The action state of the wearable action-aiding device (10-2) is reflected on the wearable type action-aiding device (10-1). Thus, the doctor (1) can feel the state of the patient (2) objectively.
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Background: Myoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these prob-lems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.
Methods: Tested in five volunteers, a redundant arrangement was obtained with two pairs of electrodes for each recording channel. The myoelectric signals were electronically amplified, filtered and digitalized, while the processing, fusion algorithms and control were implemented in a personal computer under MATLAB® environment and in a Digital Signal Processor (DSP). The experiments used an industrial robotic manipulator BOSCH SR-800, type SCARA, with four degrees of freedom; however, only the first joint was used to move the end effector to a desired position, the latter obtained as proportional to the EMG amplitude.
Results: Several trials, including disconnecting and reconnecting one electrode and disturbing the signal with synthetic noise, were performed to test the fusion techniques. The results given by VWA and DKF were transformed into joint coordinates and used as com-mand signals to the robotic arm. Even though the resultant signal was not exact, the failure was ignored and the joint reference signal never exceeded the workspace limits. Conclusion: The fault robustness and safety characteristics of a myoelectric controlled
manipulator system were substantially improved. The proposed scheme prevents potential risks for the operator, the equipment and the environment. Both algorithms showed efficient behavior. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators to better assure the system functionality when electrode faults or noisy environment are present.

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Conventional neurorehabilitation appears to have litt-le impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilita-tion has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage, intensity, and ti-me of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and ap-plication of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabili-tation. Particular attention is paid to the idea of context, task generalization and training schedule. The assumptions that underlie the choice of both movement trajectory programmed into the robot and the de-gree of active participation required by subjects are examined. We consider rehabilitation as a general learning problem, and examine it from the perspective of theoretical learning frameworks such as super-vised and unsupervised learning. We discuss the limitations of current robotic neurorehabilitation para-digms and suggest new research directions from the perspective of computational motor learning.

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Обычный мужчина, как правило, способен помочь подняться лежащему человеку. Если, конечно, лежащий — не сумоист. Но медсестрами зачастую работают женщины, для которых помочь пациенту бывает довольно проблемно. Именно для таких случаев и предназначен экзоскелет Atlas.

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A previous study from our laboratory showed that when soleus electromyography was used to control the amount of plantar flexion assistance from a robotic ankle exoskeleton, subjects significantly reduced their soleus activity to quickly return to normal gait kinematics. We speculated that subjects were primarily responding to the local mechanical assistance of the exoskeleton rather than directly attempting to reduce exoskeleton mechanical power via decreases in soleus activity. To test this observation we studied ten healthy subjects walking on a treadmill at 1.25 m/s while wearing a robotic exoskeleton proportionally controlled by medial gastrocnemius activation. We hypothesized that subjects would primarily decrease soleus activity due to its synergistic mechanics with the exoskeleton. Subjects decreased medial gastrocnemius recruitment by 12% (p < 0.05) but decreased soleus recruitment by 27% (p < 0.05). In agreement with our hypothesis, the primary reduction in muscle activity was not for the control muscle (medial gastrocnemius) but for the anatomical synergist to the exoskeleton (soleus). These findings indicate that anatomical morphology needs to be considered carefully when designing software and hardware for robotic exoskeletons.

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