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

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Integrating humans and robotics technology into one system offers multiple opportunities for creating assistive technologies that can be employed in biomechanical, industrial, and aerospace applications. The present study deals with employing the adaptive neuro-fuzzy inference system (ANFIS) approach in rule base derivation for powered exoskeleton intelligent control to assist paraplegic patient mobility. By employing the hybrid learning algorithm, optimal distributed membership functions to describe the mapping relation in the input and output parameters of the gait cycle were derived. As the proposed control strategy was unaffected by changing human dynamics, the reliability and robustness of the controller for safe interaction with humans were increased.

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This paper proposes an electromyography (EMG) signal based control method for a seven degrees of freedom (7DOF) upper-limb motion assist exoskeleton robot (SUEFUL-7). The SUEFUL-7 is able to assist the motions of shoulder vertical and horizontal flexion/extension, shoulder internal/external rotation, elbow flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation of physically weak individuals. In the proposed control method, an impedance controller is applied to the muscle-model-oriented control method by considering the end effector force vector. Impedance parameters are adjusted in real time by considering the upper-limb posture and EMG activity levels. Experiments have been performed to evaluate the effectiveness of the proposed robotic system.

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An investigation of the control of a multifingered robot hand, the Utah/MIT dextrous hand, with a hand exoskeleton worn by a human is presented. The hand exoskeleton used was the EXOS Handmaster. The relative effectiveness of two approaches in performing a task in which an object is grasped with the finger tips of the dexterous hand at the same location as the human’s grasp is evaluated. One approach was to directly measure the joint angles from the Handmaster and transfer these as the desired joint positions for the robotic fingers. The other approach was to develop a mathematical model that would allow the finger tips of the slave to replicate the finger tip positions of the human while wearing the Handmaster. This was accomplished by developing a kinematic model of the human finger and thumb and of the EXOS Handmaster. The actual finger tip position was computed within the accuracy of the model

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In this paper, a real-time upper limb’s motion tracking exoskeleton device is proposed and developed, which can be used in active rehabilitation of upper limb for stroke. This device is portable and wearable and is potential to be used in the passive and active training for stroke patients. Passive training has been finished in previous work and in this paper, the preliminary work for active rehabilitation is presented, which is realization of the real-time upper limb’s motion tracking. We use an adaptive weighted average filtering and double close-loop control to realize real-time and stable tracking performance. Experiments have been conducted to prove that it is suitable for active rehabilitation.

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