Bayesian human intention estimator for exoskeleton system

The estimation of the human applying torque is critical in many applications, especially in the design of assistive exoskeleton. The most common approaches are the estimation by the inverse dynamics or by the EMG signal. However, the EMG-based torque estimation is not always stable owing to the sweats of skin, the noise from posture change, and the nonlinear mapping between the EMG signal and the human torque. In addition, the estimation based on the dynamic model is unstable in the multi-DOFs system and especially in the existence of exogenous disturbance, such as ground reaction force. Therefore, we propose the Bayesian human intention estimator and the graphical model of human-exoskeleton system to solve these issues. Through the experiments, the proposed method can merge the information from both the EMG signal and dynamic model, and can make the estimated torque more stable.