Human-in-the-Loop Weight Compensation and Mass Estimation in Upper Limb Wearable Robots Towards Muscles’ Effort Minimization

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.

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