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