A high-quality haptic interface is typically characterized by low apparent inertia and damping, high structural stiffness, minimal backlash, and absence of mechanical singularities in the workspace. In addition to these specifications, exoskeleton haptic interface design involves consideration of space and weight limitations, workspace requirements, and the kinematic constraints placed on the device by the human arm. These constraints impose conflicting design requirements on the engineer attempting to design an arm exoskeleton. In this paper, the authors present a detailed review of the requirements and constraints that are involved in the design of a high-quality haptic arm exoskeleton. In this context, the design of a five-degree-of-freedom haptic armexoskeleton for training and rehabilitation in virtual environments is presented. The device is capable of providing kinesthetic feedback to the joints of the lower arm and wrist of the operator, and will be used in future work for robot-assisted rehabilitation and training. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. As a training tool, the device provides a means to implement flexible, repeatable, and safe training methodologies.

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The IHMC Mobility Assist Exoskeleton is a robotic suit that a user can wear for strength augmentation or gait generation. This first generation exoskeleton prototype focuses on providing walking assistance to persons with lower extremity paralysis. The main goal is to successfully enable a person that cannot walk without assistance to walk in a straight line a distance of 15 feet. When in disable assist mode this prototype will rely on the user to provide balance control, and thus an external means for balancing will be required, such as crutches or a walker. Power and control is off board and supplied to the exoskeleton by means of a tether. Rotary series elastic actuators (RSEAs), which have high force fidelity and low impedance were designed to power the joints. This paper describes the design, test results, future work and potential applications of the exoskeleton.

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The successful motor rehabilitation of stroke, traumatic brain/spinal cord/sport injured patients requires a highly intensive and task-specific therapy based approach. Significant budget, time and logistic constraints limits a direct hand-to-hand therapy approach, so that intelligent assistive machines may offer a solution to promote motor recovery and obtain a better understanding of human motor control. This paper will address the development of a lower limb exoskeleton legs for force augmentation and active assistive walking training. The twin wearable legs are powered by pneumatic muscle actuators (pMAs), an experimental low mass high power to weight and volume actuation system. In addition, the pMA being pneumatic produces a more natural muscle like contact and as such can be considered a soft and biomimetic actuation system. This capacity to «replicate» the function of natural muscle and inherent safety is extremely important when working in close proximity to humans. The integration of the components sections and testing of the performance will also be considered to show how the structure and actuators can be combined to produce the various systems needed for a highly flexible/low weight clinically viable rehabilitation exoskeleton.

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To assist physically disabled, injured, and/or elderly persons, we have been developing a 3DOF exoskeleton robot for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. The exoskeleton robot is mainly is controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move. This paper introduces the mechanism of the exoskeleton robot and also proposes a control method of the exoskeleton robot considering the generated end-effector force vectors.

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Virtual Reality describes a 3-D computer generated environment, controlled by the user from a remote location. VR has applications in robotics, entertainment and medical field. Virtual Reality robotic systems have been a major help in hazardous environments and in areas which need a high degree precision such as nuclear plants and tele-surgery. An ideal VR system immerses the user in the virtual environment. This condition is termed as telepresence. The components of a VR system are human operator, interface system and teleoperator. VR system relies on human interface performance for its high accuracy. Commercially available interfaces such as Data Gloves and exoskeleton devices provide less accuracy and restricted motion. A biocontrol interface utilizing human physiological signals such as Electromyogram (EMG) would be a natural and synergistic way of controlling a remote teleoperator. Previous studies (Suryanarayan and Reddy) have shown that surface EMG (SEMG) from flexor muscle can be effectively used as a human interface for controlling teleoperators for dynamic motion of elbow joints. The goal of the present study was to investigate the use of SEMG from extensor muscle to control real time dynamic movement of index finger at various speeds for full range. Normal subjects were asked to rhythmically flex and extend the index finger at different speeds. The actual angle was measured using a miniature accelero-meter. SEMG from extensor muscle (Extensor Digitorum Superficialis (EDS)) was used to correlate with angle made by index finger at various speeds, with all other fingers at constant position. Parameters were extracted from SEMG. Neural networks were trained with input as extracted parameters and targets as measured angles. Best five networks were recruited for each committee. Two committees for each speed were formed. The committees were evaluated using data from new subject and the errors between actual and predicted joint angle was calculated. The committees were able to predict the joint angle at different speeds. The RMS errors between the predicted and the actual angle were found to be between 3-27%. The errors were more in the flexion region as compared to the extensor region. The study demonstrated the use of SEMG from EDS for the prediction of joint angle at different speeds. It also demonstrated the use of committee neural networks (CNN) in control related prediction problems. The study has taken a step forward in the direct biocontrol of telemanipulator and VR environments using SEMG. The study would find an application in medicine and control of robotic assist devices.

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