Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable musclelevel force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named “individual muscle-force control” using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscu-lar function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data.

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Exoskeletric robotic interfaces have been employed in several fields of Human Robotics research like in rehabilitation, haptic interaction, teleoperation and space applica-tions. In this paper a novel Hand Exoskeleton (HE) is presented. Such device is optimized for haptic inter-action with virtual environment and allows exerting high accuracy controlled forces on the fingertip of the index and thumb of the operator. Several design solutions are conceived and implemented for optimizing the accuracy and mechanical performances. The use of Remote Centers of Motion mechanisms allows achieving compactness and lightweight. Moreover an improved stiffness of the transmission and reduced requirements for the electromechanical actuators have been achieved thanks to a novel Patent Pending principle for integrating speed reduction ratio within the transmission system. The mechanical design and some preliminary characterization tests for this device are shown.

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Background: To improve design of robotic lower limb exoskeletons for gait rehabilitation, it is critical to identify neural mechanisms that govern locomotor adaptation to robotic assistance. Previously, we demonstrated soleus muscle recruitment decreased by ~35% when walking with a pneumatically-powered ankle exoskeleton providing plantar flexor torque under soleus proportional myoelectric control. Since a substantial portion of soleus activation during walking results from the stretch reflex, increased reflex inhibition is one potential mechanism for reducing soleus recruitment when walking with exoskeleton assistance. This is clinically relevant because many neurologically impaired populations have hyperactive stretch reflexes and training to reduce the reflexes could lead to substantial improvements in their motor ability. The purpose of this study was to quantify soleus Hoffmann (H-) reflex responses during powered versus unpowered walking. Methods: We tested soleus H-reflex respon-ses in neurologically intact subjects (n=8) that had trained walking with the soleus controlled robotic ankle exoskeleton. Soleus H-reflex was tested at the mid and late stance while subjects walked with the exoskeleton on the treadmill at 1.25 m/s, first without power (first unpowered), then with power (powe-red), and finally without power again (second unpowered). We also collected joint kinematics and electro-myography. Results: When the robotic plantar flexor torque was provided, subjects walked with lower soleus electromyographic (EMG) activation (27-48%) and had concomitant reductions in H-reflex amplitude (12-24%) compared to the first unpowered condition. The H-reflex amplitude in proportion to the background soleus EMG during powered walking was not significantly different from the two unpowered conditions. Conclusion: These findings suggest that the nervous system does not inhibit the soleus H-reflex in response to short-term adaption to exoskeleton assistance. Future studies should determine if the findings also apply to longterm adaption to the exoskeleton.

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We propose a novel self-feeding robotic system that is suitable to handle Korean food including sticky rice. A self-feeding robotic system allows people with disabilities of upper limbs to eat the chosen food when they want it. The proposed robotic system consists of two arms: one is a grab-arm, and the other is a spoon-arm. The grab-arm chooses desired food from a food container, and the spoon-arm provides suitable feeding intervals for users. The merits of a proposed system are easy to handle sticky rice, compatibility of existing food containers, such as bowls, and modularly applications for the economical point of view. We concentrate on Korean people with the upper limbs’ disability and Korean food. We describe design concepts and an overall system structure with above mentioned merits.

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Human upper-limb is involved in many daily human activities. The human intention for upper-limb motions can be estimated based on the activation pattern of upper-limb muscles. The upperlimb muscle activities during basic upper-limb motions and the daily upper-limb motions have been studied to enable power-assist robotic exoskeleton systems to estimate human upper-limb motions based on the electromyographic (EMG) signals of related muscles. The relationship between the upperlimb motions and the activity levels of main related muscles concerning the daily upper-limb motions are analyzed in this study. The muscle combinations are identified to separate some basic motions of upper-limb. The EMG signals of identified muscles will be used as input information to design a controller of the upper-limb power-assist robotic exoskeleton systems.

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