Abstract
Neuroprosthetic devices that use transcutaneous neuromuscular electrical stimulation (NMES) are potential interventions to restore skeletal muscle function in people with neurological disorders. As commonly noted, how to assess the NMES-induced muscle fatigue is a critical problem. This is because the capability of fatigue assessment is a necessary precursor for optimally modulating the NMES dosage to improve the control performance of a neuroprosthesis and ensure user’s safety. To effectively estimate the NMES-induced muscle fatigue, this paper proposes a novel state observer that combines a mathematical predictive fatigue model and intermittent feedback from ultrasound-derived strain images. The strain images quantify muscle contractility during NMES. Principal component regression (PCR) is used to derive a relationship between the strain images and instantaneous muscle force production. Lyapunov stability analysis was performed to obtain the convergence property of the designed observer. A globally uniformly ultimately bounded (GUUB) result was obtained. Simulations based on pre-recorded data from a human experiment were also conducted to demonstrate the performance of the designed observer.
