The availability of accurate and comprehensive models of human limbs, combining high reliability and real-time operation, is required to develop seamless and intuitive human-machine interfaces. Biomechanist have developed complex models of the human lower limb, combining kinematics and kinetics data with neural signals for the purpose of studying human motor control strate-gies. The complexity prevent their application to situations with stringent real-time requirements. We are currently working on the enhancement of an EMG-driven model of the human lower extremity to achieve compre-hensiveness, accuracy, and fast runtime execution. Starting from the very complex model developed by Lloyd et al. we have evaluated how this model can be enhanced to achieve higher performances in terms of compu-tation time with no loss of prediction accuracy. The enhanced model will be applied to the control of powered orthosis and to the development of advanced biomimetic control systems for humanoids robots. We started our investigation with the analysis of the impact of modeling the tendon as an infinitely stiff body and quantitatively evaluated the changes in the behavior of the modified model w.r.t. the original one. We also integrated a runtime anatomical model that allowed to execute the whole EMG-driven model at runtime. This is a significant improvement as the previous available model could not entirely be executed at runtime due to the complexity of the original anatomical model.

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