This paper reports the development of a power-assisted suit system actuated by ultrasonic motors. The proposed assisted suit is designed to support a caretaker performing nursing care tasks. The structure of the assisted suit has been designed to cover the caretaker’s motions as much as possible, and the suit is equipped with ultrasonic motors mounted in areas close to the joint rotation axis of the elbow, shoulder, waist, and knee. The suit frame is designed as an exoskeleton structure that can protect the wearer from bearing the suit’s weight because the foot part contacts the ground. A Phi60 double sandwich-type motors are used for the upper body part of the assisted suit. The torque of the double sandwich-type motor is about 3.8 times compared to the torque of the single sandwich-type motor. From the above results, the double sandwich-type motor is less space-saving but has high torque efficiency, and is therefore suitable as the actuator for the upper part of the assisted suit.

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The article presented the modeling and control of a pneumatically-powered gait orthosis (PPGO). According to the mechanism ofexoskeleton structure of pneumatic gait training device and pneumatic proportional system, an appropriate nonlinear process model was derived. In order to provide assistance as needed for supporting patient’s locomotion, a predictive fuzzy control (PFC) method was proposed based on the nonlinear dynamic model. This control algorithm is to evaluate the variables of PPGO based on fuzzy inference mechanism. The experiment of locomotion has been done to verify the effectiveness of proposed controller on the experimental setup of a pneumatically-powered gait orthosis.

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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, estimation of distribution algorithm(EDA) is used to identification interaction parameters. Second-order EDA based on general structure Gauss network is introduced to replace the condition probability density function, the crossover and mutation operators are added to speed the evolution process. Combining the individual energy-entropy selection, the detail human-machine interaction forces identification method using the improved estimation of distribution algorithm is given and the human-machine carrying robot control system simulation results show the validity of the method.

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This paper presents the design, and performance of a high fidelity three degree-of-freedom wrist exoskeleton robot, for neuroscience study, training and rehabilitation. The IIT-Wrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuriesIn the present paper the IIT-Wrist haptic robot is described in terms of kinematics and haptics features to meet specific requirements for a safety human.machine interaction. In relation with a feasibility study in the field of robot therapy a preliminary training of stroke patient was perfrormed. The task consisted in tracking a target using one degree of freedom at time: Flexion/Extension, adduction/abduction, pronation/supination separately. The target motion is harmonic and tracking is aided by a suitable force field. The preliminary study with three patients shows the stability and the efficacy of the control scheme.

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In this paper we introduce the use of force feedback in conjunction with myoelectric control to establish an improved interface for a powered prosthetic limb. The force feedback is delivered through a single-axis exoskeleton worn about the elbow, while the EMG signal is derived from the biceps muscle. This combination is intended to produce a sense of effort in the biceps that is associated with the action of the motorized prosthetic gripper. The method engages both efferent and afferent signals innervating a functional muscle with the aim of realizing a muscle that is effectively biarticular. The controlling muscle spans one joint physiologically and a second, prosthetic joint functionally. Preliminary experiments have demonstrated that force feedback can substitute for vision during grasp and lift tasks.

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