This paper presents the design of an assistive real-time system for the upper limb disabled to access a computer via residual muscle activities without standard computer interfaces (e.g. a mouse and a keyboard). For this purpose, electromyogram (EMG) signals from muscles in the lower arm were extracted and filtered using signal statistics (mean and variance). In order to control movement and clicking of a cursor from the obtained signals, six patterns were classified, applying a supervised multi-layer neural network trained by a backpropagation algorithm. In addition, an on-screen keyboard was developed, making it possible to enter Roman and Korean letters on the computer. Using this computer interface, the user can browse the Internet and read/send e-mail. The developed computer interface provides an alternative means for individuals with motor disabilities to access computers. A possible extension of our interface methodology can be incorporated in controlling bionic robot systems for the limb disabled (e.g.exoskeletons, limb prostheses).

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Assistive knee brace is a species of wearable lower extremity exoskeletons. Such assistive equipment can enhance people’s strength and provide desired locomotion to have advantages over wheelchairs, which are commonly used for patients with mobility disorders. However, the integration between the assistive knee brace and the user is challenging as inaccurate alignments may adversely affect the biomechanics of the knee joint. The goal of this study is to evaluate the changes between normal walking and walking with an assistive knee brace in “off” mode. The assistive knee brace was developed by integrating a multifunctional actuator with a custom-made knee-ankle-foot orthosis in order to minimize excessive shifting and to improve alignment to the knee joint. Spatial and temporal gait parameters, joint kinematics and joint kinetics parameters were compared. In general, the observed results showed that most of the gait parameters were not affected when walking with the knee brace. The only significant differences were found in knee flexion and knee rotational motions. These results indicated that walking with the developed knee brace provided minimal hindrance to the user and assured that assistive torque can be applied to the knee joint.

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This paper describes the design and development of a new actuator with adjustable stiffness (AwAS) which can be used in robots which are necessary to work close to or physically interact with humans, e.g. humanoids and exoskeletons. The actuator presented in this work can independently control equilibrium position and stiffness by two motors. The first motor controls the equilibrium position while the second motor regulates the compliance. The novelty of the proposed design with respect to the existing systems is on the principle used to regulate the compliance. This is done not through the tuning of the pretension of the elastic element as in the majority of existing system but by controlling the fixation of the elastic elements (springs) using a linear drive. An important consequence of this approach is that the displacement needed to change the stiffness is perpendicular to the forces generated by the springs, thus this helps to minimize the energy/power required to change the stiffness. This permits the use of a small motor for the stiffness adjustment resulting in a lighter setup. Experimental results are presented to show the ability of AwAS to control position and regulate the stiffness independently.

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To harness the increased dexterity and sensing capabilities in advanced prosthetic device designs, amputees will require interfaces supported by novel forms of sensory feedback and novel control paradigms. We are using a motorized elbow brace to feed back grasp forces to the user in the form of extension torques about the elbow. This force display complements myoelectric control of grip closure in which EMG signals are drawn from the biceps muscle. We expect that the action/reaction coupling experienced by the biceps muscle will produce an intuitive paradigm for object manipulation, and we hope to uncover neural correlates to support this hypothesis. In this paper we present results from an experiment in which 7 able-bodied persons attempted to distinguish three objects by stiffness while grasping them under myoelectric control and feeling reaction forces displayed to their elbow. In four conditions (with and without force display, and using biceps myoelectric signals ipsilateral and contralateral to the force display,) ability to correctly identify objects was significantly increased with sensory feedback.

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The E-GRIP grasping device enables a wearer, with limited manual strength and dexterity, to grip implements with handles such as golf clubs, brooms, rakes, and shovels. Grip strength is achieved by unique usage of externally located shape memory metal wires. The device is also voice controlled for hands free operation

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