Knee Joint Torque Prediction with Uncertainties by a Neuromusculoskeletal Solver-informed Gaussian Process Model

Abstract

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is rapidly growing, yet uncertainty in predicted torque capacity can significantly impact the user-exoskeleton interface safety. In this paper, we estimated knee flexion/extension torques by using a neuromusculoskeletal (NMS) solver-informed Gaussian process (NMS-GP) model with muscle electromyography signals and joint kinematics as model inputs. The NMS-GP model combined the NMS and GP models by integrating valuable features from an NMS solver into a GP model. The NMSGP model was used to predict knee joint torque in daily activities with uncertainty quantification. The activities included slow walking, self-selected speed walking, fast walking, sit-to-stand, and stand-to-sit. Model performance, defined as low prediction error between the model's predicted torque and measured torques from inverse dynamics computations, of both the NMS-GP and NMS models was analyzed. We found that prediction error was significantly lower in NMS-GP models than in NMS models. We observed relatively high uncertainties in the predicted knee torque at the beginning of each movement, particularly in self-selected speed walking. High uncertainties were also found during terminal stance and swing in self-selected speed walking. Compared to other torque prediction methods, the proposed NMS-GP model not only provides an accurate joint torque prediction but also a measure of the uncertainty. Our study showed that the NMS-GP model has a large potential in control strategy design for rehabilitation exoskeletons and to enhance the overall user experience.