The author describes a newly developed mechanical hand system. The robot hand is in human-like configuration with a thumb and three fingers, a palm, a wrist, and the forearm in which the hand and wrist actuators are located. Each finger and the wrist has its own active electromechanical compliance system, allowing the joint drive trains to be stiffened or loosened. This mechanism imitates the human muscle dual function of positioner and stiffness controller. This is essential for soft grappling operations. The hand-wrist assembly has 16 finger joints, three wrist joints, and five compliance mechanisms for a total of 24 degrees of freedom. The strength of the hand is roughly half that of the human hand and its size is comparable to a male hand. The hand is controlled through an exoskeleton glove controller that the operator wears. The glove provides the man-machine interface in telemanipulation control mode: it senses the operator’s inputs to guide the mechanical hand in hybrid position and force control. The hand system is intended for dexterous manipulations in structured environments. Typical applications will include work in hostile environments such as space operations and nuclear power plants

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Robotic training following stroke is an emerging rehabilitation technique to facilitate neuromuscular plasticity for regaining functional movements. Most existing training robots follow a defined task-based trajectory in assistive or resistive mode. Whilst this approach may be effective in certain cases it does not allow training of arm or leg in a natural way as the motion is precisely guided by the robot or exoskeleton. The ideal training would be to allow arm/leg follow a trajectory naturally without any augmented support and bring it back when the limb is diverted significantly from the goal. This paper presents implementation of this approach in a virtual environment using a simple force feedback joystick. This will guide the arm within a tunnel of trajectory by providing assistance or resistance depending on location of the arm. The technique can be extended for 3-dimensoional arm movement which can help learn neural plasticity naturally.

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To complete the control of exoskeleton carrying robot perfectly, the human-machine interaction forces model should be identified, which can be simulated using spring-damper model, that is, the coefficient elasticity and damping should be gotten. For the coupling of the several joints, the parameters should be optimized from the system global performance. In this paper, genetic algorithm is used to identification interaction parameters. Pseudo-gradient is introduced and the individual pseudo-gradient justification is used in genetic algorithm. Annealing selection according to the fitness is given to keep the population diversity, and the memory mechanism is added to speed up evolution. Combing the characteristics of the lower extremity carrying robot walking, the detail human-machine interaction forces identification method using the improved genetic algorithm is given and the quasi-Newton iterative learning control simulation results show the validity of the method.

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One of the useful features of robotic rehabilitation is the possibility of movement quantification, which is currently lacking in conventional rehabilitation therapy. Movement performance measures calculated from this quantitative information serves various purposes — (a) a good supplement to clinical assessment measures, (b) can be more sensitive than many clinical measures which use ordinal scales for scoring, (c) can be used to track a patient’s recovery over time. Our research group has developed a 5 degree-of-freedom wearable exoskeleton robot for upper-extremity rehabilitation (RUPERT); RUPERT provides movement kinematics information in the form of joint angles, and also provides the pressure inside the pneumatic muscle actuators that drive the robot. In this paper we describe some useful robot-measured performance metrics that can be calculated from the sensor information collected from RUPERT. Some of the important performance-metrics described in this paper are — (a) Amount-of-assistance, (b) Smoothness, and (c) Movement synergy. We present a new method for calculating smoothness, which is uses a very different approach from some of the currently available approaches for calculating smoothness.; we call this approach the dasiaspectral methodpsila, which looks at the frequency spectrum of the movement velocity signal to estimate movement smoothness. In addition, we also present a method to analyze the effect of target location and DOF on the performance metrics, and also a method to detect fatigue.

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This paper presents the development of a neuromuscular interface for an exoskeleton to assist the elbow joint. The interface uses electromyographic (EMG) signals obtained from the biceps and triceps to predict elbow flexion and extension movements. These movements occur in the sagittal plane and the effects of forearm weight have been incorporated. The interface uses a physiological-model-based approach to convert the EMG signals to a joint displacement and this is based on Hill-type muscle models that have traditionally been used in clinical applications or for diagnosing and managing neurological and orthopaedic conditions. Simulation results have been obtained for the performance of the interface on pre-recorded data. While the general trend of movement was correctly identified by the interface, further experiments are required to quantify the accuracy and determine real-time performance capabilities.

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