We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.

We have been developing exoskeletons (exoskeletal robots) for assisting the motion of physically weak persons such as elderly persons or slightly disabled persons in daily life. In this paper, we propose a 3 DOF exoskeleton and its control system to assist the human upper-limb motion (shoulder joint motion and elbow joint motion) of physically weak persons. The proposed robot automatically assists the human motion mainly based on the skin surface electromyogram (EMG) signals. Fuzzy control has been applied to realize the sophisticated real-time control of the exoskeleton. Experiment has been performed to evaluate the proposed exoskeleton.

We develop exoskeletal robots for the motion support of physically weak persons. In this study, the human elbow motion is assisted by the exoskeletal robot system, since the elbow motion is one of the simplest and the most important motion of human beings in everyday life activities. The angular position and impedance of the exoskeletal robot system are controlled by multiple fuzzy-neuro controllers. The skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion are used as input information of the controller. Since the activation level of working muscles tends to vary with the human subject's arm posture, an intelligent interface that cancels out the effect of posture changes of the human subject's arm is proposed in this paper. The experimental results show the effectiveness of the proposed intelligent interface.