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Active power-assist exoskeletons are becoming more prospective than follow-up types, especially for elderly and handicapped motion auxiliary. The exoskeleton is required not only to withstand load but also to actively share the weight of a human body. Active power-assist lower limb is designed to meet this expectation. The definition of ''active powerassist'' was suggested in this article. A unique man-machine motion mapping was derived based on the configuration matching, wherein the exoskeleton obtains the wearer's motion data and parse out the corresponding intention. Manmachine coupling mechanisms were exquisitely configured, which rationalize the degrees of freedom of the manmachine system and facilitate force transmission for active assistant. The dynamic knees and hip joints with the integrated force servo unit were designed, which is the key to realize soft contact and cooperative movement. The prototype was developed, and three basic functions (human movement perception, force transmission, and movement cooperation) were preliminarily verified in a single leg swinging experiment. The effect of follow-up mode and active power-assist mode were quantitatively analyzed in marches-on-the-spot experiment. A 24.6% proportional reduction of the wearer foot force and smooth man-machine coordination in field experiments has demonstrated the feasibility of this structure design of active power-assist lower limb.

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Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.