Due to the complexity and high degrees of freedom, the detailed assessment of human biomechanics is necessary for the design and optimization of an effective exoskeleton. In this paper, we present full kinematics, dynamics, and biomechanics assessment of unpowered exoskeleton augmentation for human running gait. To do so, the considered case study is the assistive torque profile of I-RUN. Our approach is using some extensive data-driven OpenSim simulation results employing a generic lower limb model with 92-muscles and 29-DOF. In the simulation, it is observed that exoskeleton augmentation leads to $$4.62\%$$ 4.62 % metabolic rate reduction for the stiffness coefficient of $$\alpha ^*=0.6$$ α ∗ = 0.6 . Moreover, this optimum stiffness coefficient minimizes the biological hip moment by $$26\%$$ 26 % . The optimum stiffness coefficient ($$\alpha ^*=0.6$$ α ∗ = 0.6 ) also reduces the average force of four major hip muscles, i.e., Psoas, Gluteus Maximus, Rectus Femoris, and Semimembranosus. The effect of assistive torque profile on the muscles’ fatigue is also studied. Interestingly, it is observed that at $$\alpha ^{\#}=0.8$$ α # = 0.8 , both all 92 lower limb muscles’ fatigue and two hip major mono-articular muscles’ fatigue have the maximum reduction. This result re-confirm our hypothesis that ”reducing the forces of two antagonistic mono-articular muscles is sufficient for involved muscles’ total fatigue reduction.” Finally, the relation between the amount of metabolic rate reduction and kinematics of hip joint is examined carefully where for the first time, we present a reliable kinematic index for prediction of the metabolic rate reduction by I-RUN augmentation. This index not only explains individual differences in metabolic rate reduction but also provides a quantitative measure for training the subjects to maximize their benefits from I-RUN.

In this paper, we target multiple goals related to our passive running assistive device, called I-RUN. The major goals are: (1) finding the main reason behind individual differences in benefiting from our assistive device at the muscles level, (2) devising a simple measure for on-line I-RUN stiffness tuning, and creating a lab-free simple kinematic measure for (3) estimating metabolic rate reduction as well as (4) training subjects to maximize their benefit from I-RUN. Our approach is using some extensive data-driven OpenSim simulation results employing a generic lower limb model with 92-muscles and 29-DOF.It is observed that there is a significant relation between the hip joints kinematic and changes in the metabolic rate in the presence of I-RUN. Accordingly, a simple kinematic index is devised to estimate metabolic rate reduction. This index not only explains individual differences in metabolic rate reduction but also provides a quantitative measure for training subjects to maximize their benefits from I-RUN.The simulation results also re-confirm our hypothesis that “reducing the forces of two antagonistic mono-articular muscles is sufficient for involved muscles’ total effort reduction”. Consequently, we introduce a two-muscles EMG-based metric for the on-line tuning of I-RUN.

In this paper: (1) We present a novel humanin-the-loop adaptation method for whole arm muscles' effort minimization by means of weight compensation in the face of an object with an unknown mass. (2) This adaptation rule can also be used as a cognitive model for the identification of mass value using EMG sensors. (3) This adaptation rule utilizes the activation (myoelectric) signal of only four muscles in the upper limb to minimize the whole muscles' effort. We analytically discuss the stability, optimality, and convergence

In this paper: (1) We present a novel human-in-the-loop adaptation method for whole arm muscles' effort minimization by means of weight compensation in the face of an object with unknown mass. (2) This adaptation rule can also be used as a cognitive model for the identification of mass value. (3) This adaptation rule utilizes the EMG signal of only four muscles in the upper limb to minimize the whole muscles' effort. The method is analyzed from analytical, simulation, and experimental perspectives. We analytically discuss the stability, optimality, and convergence of the proposed method. This method's effectiveness for whole muscles' effort reduction is studied by simulations (OpenSim) on a generic and realistic model of the human arm, a model with 7-DOF and 50 Hill-type-muscles. In addition, the applicability of this method in practice is experimented with by a 2-DOF arm assist device for two different tasks; static (holding an object) and cyclic (reaching point-to-point) tasks. The simulations and experimental results show the presented method's performance and applicability for weight compensation in upper limb assistive exoskeletons. In addition, the simulations in OpenSim completely support that the suggested set of mono-articular muscles are sufficient for whole muscles' effort reduction.