This paper deals with the optimal design and control of an exoskeletal robot. First, the motion data from the fingers of a normal subject was captured by a vision system. As the human finger joints cannot be modeled by single revolute joints due to changing instantaneous centre of rotation, we have used 4-bar mechanisms to model each joint. Optimal 4-bars have been designed using genetic algorithms, by minimizing the error between a coupler point and points traced by the finger links. It is shown that the designed 4-bars can accurately track the motion of the human fingers. The exoskeleton is controlled by using the EMG signals obtained from the subject’s muscles. The relation between the EMG and finger motion is first learned, using a neural net. Based on the learned parameters, the subjects EMG signal is used to control a simulation of the exoskeleton joint motion. A comparison between Recurrent Neural Network and Multi Layer Perceptron for classifying and mapping the EMG to finger position was also carried out.

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Many kinds of exoskeleton robots have been developed to assist the motion in rehabilitation and daily living of physically weak persons. The concept of perception-assist has been proposed to assist not only the daily motion but also the interaction with an environment, by applying the modification force to the user’s motion. In this paper, task-oriented perception-assist is proposed to assist the daily task of physically weak persons with an upper-limb power-assist exoskeleton. The effectiveness of the proposed method has been evaluated by performing experiments.

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Over 450,000 Americans suffer from degenerative muscle diseases characterized by loss of strength and dexterity in the human hand. An assistive hand exoskeleton was designed to amplify residual muscle strength and restore functionality by assisting pinching and grasping motions. The device featured three movable digits: thumb, index, and middle-ring-small (MRS) digit. Adjustable straps wrapped around the exterior of the finger links and secured the user’s fingers inside the device. A microcontroller processed force sensing resistor (FSR) data and commanded articulating motors. The exoskeleton was lightweight, flexible, portable and accessible to a wide range of user finger diameters.

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This paper presents a human—machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.

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This paper describes the design and human machine interface of an Active Leg EXoskeleton (ALEX) for gait rehabilitation of patients with walking disabilities. The paper proposes force-field controller which can apply suitable forces on the leg to help it move on a desired trajectory. The interaction forces between the subject and the orthosis are designed to be ‘assist-as-needed’ for safe and effective gait training. Simulations and experimental results with the force-field controller are presented. Experiments have been performed with healthy subjects walking on a treadmill. It was shown that a healthy subject could be retrained in about 45 minutes with ALEX to walk on a treadmill with a significantly altered gait. In the coming months, this powered orthosis will be used for gait training of stroke patients.

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