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 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 of the proposed method. The effectiveness of this method for whole muscles effort reduction is studied by simulations (OpenSim software) on a generic and realistic model of the human arm, a model with 7-DOF and 50 Hill-type-muscles. The simulation results show the presented methods performance and applicability for weight compensation and mass estimation in upper limb assistive robots. In addition, the simulations in OpenSim completely support that the suggested set of mono-articular muscles are sufficient for whole muscles effort reduction.