Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we present a new technique for the upper limb power exoskeleton robot in which load is gripped by the human subject and not by the robot while the robot assists. Main challenge is to find non-biological signal based human desired motion intention to assist as needed. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the force sensors is used to estimate the human interactive force from which desired human motion is extracted using adaptive Radial Basis Function Neural Network (RBFNN). Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five DOF are passive while two are active. Active joints include shoulder and elbow in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joints. To ensure high quality performance model reference based adaptive impedance controller is employed. Exoskeleton performance is evaluated experimentally by a neurologically intact subject which validates the effectiveness.

Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we focus on developing new technique for the upper limb power exoskeleton in which load is handled by the human subject and not by the robot. Main challenge along with the design complexity is to find the desired human motion intention and to develop an algorithm to assist as needed accordingly. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the load cells is used to estimate the desired human intention by which desired trajectory is generated. The desired trajectory is then tracked by passivity based adaptive control technique. Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five are passive and two are active. Active joints include shoulder and elbow, powered by electric motors and move in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joint. Performance of the exoskeleton is evaluated experimentally by a neurologically intact subject. The results show that after adjusting the motion intention recognition algorithm for the subject, the robot assisted effectively and the subject only felt nominal load regardless of the weight in hand.