Body weight supported treadmill training (BWSTT) has been confirmed to be an effective gait rehabilitation therapy for patients with locomotor dysfunction of the lower limbs. Powered Gait Orthosis (PGO) is a pair of powered mechanical legs in exoskeleton structure, which can guide the patient’s legs to move in a preprogrammed physiological gait pattern during BWSTT. A prototype of single-leg PGO has been designed and constructed. It has linear actuators at hip and knee joints; it is also instrumented with sensors and encoders. To realize the physiological gait movement of the single-leg PGO, a control platform, a safe protection device, and a set-point gait motion control method have been developed. The effectiveness of the proposed set-point gait motion control method is confirmed by the preliminary experimental results..

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Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG) sig-nals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displace-ment, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to mea-sure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode dis-placement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Re-sults It was shown that mean adjusted coefficient of determination values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean values between 64% to 74% for different models. Conclusions Model estimation accu-racy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estima-tion accuracy. Among the models compared, ordinary least squares linear regression model (OLS) was shown to have high isometric tor-que estimation accuracy combined with very short training times

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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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