A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

Abstract

Neuromuscular electrical stimulation (NMES) is a promising technique to artificially activate muscles as a means to potentially restore the capability to perform functional tasks in persons with neurological disorders. A pervasive problem with NMES is that overstimulation of the muscle (among other factors) leads to rapid muscle fatigue, which limits the use of clinical and commercial NMES systems. The objective of this article is to develop an NMES controller that incorporates the effects of muscle fatigue during NMES-induced non-isometric contraction of the human quadriceps femoris muscle. Our previous work that used the RISE class of non-linear controllers cannot accommodate fatigue and muscle activation dynamics. A totally new control design approach and associated stability proof is required to derive a new class of NMES control design that accounts for muscle fatigue dynamics and a first-order activation dynamics, in addition to the second-order musculoskeletal dynamics. Motivated from a control method for robotic systems in a strict-feedback form, a backstepping based-non-linear NMES controller was designed to accommodate for the additional muscle activation dynamics. Further, experimentally identified estimates of the fatigue and activation dynamics were incorporated in the control design. The developed controller uses a neural networkbased estimate of the musculoskeletal dynamics and error due to fatigue estimation. A globally uniformly ultimately bounded stability is proven the new controller that accounts for an uncertain non-linear muscle model and bounded non-linear disturbances (e.g., spasticity and changing load dynamics). The developed controller was validated through experiments on the left and right legs of 3 able-bodied subjects and was compared with a proportional-derivative (PD) controller and a PD augmented with a neural network. The statistical analysis showed improved control performance compared with the PD controller.

Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation

“…According to the muscle fatigue dynamics and its solution mentioned in [ 13 ], an exponential regression model was used to fit the curve between the normalized sub-maximal dorsiflexion force or angle and the index number of contractions ( / ), as well as the curve between the normalized sub-maximal ERC and the index number of contractions ( / ). The coefficients of the exponential regression models were determined by using the Levenberg–Marquardt nonlinear least squares algorithm [ 43 ].…”

“…Due to the non-selective stimulation nature of FES, peripheral motor units are synchronously activated and discharged, causing the stimulated muscle to fatigue easily. The induced fatigue results in the deterioration of the muscle contraction force generation, causing a rapid loss of FES control effectiveness [ 13 ].…”

Adaptive Sliding Mode Feedback Control Algorithm for a Nonlinear Knee Extension Model

“…The fatigue effect causes muscle torque to decrease over time, and when the overstimulation effect persists, the fatigue effect increases [53]. Fatigue is implemented as a multiplier that modifies the electrically stimulated quadricep muscles' torque [3].…”

“…Fuzzy logic was used as a state variable representation. Sharma et al (2017) employed integrator backstepping with a neural network used to estimate the state variables and the second-order SM controller to control knee extension in the presence of unknown dynamic nonlinearities and specific nonlinearities of fatigue and spasticity [53]. Cousin et al (2022) used an SM with a feedforward neural network as an adaptive admittance controller for cadence rehabilitation to deal with unknown and dynamic nonlinearities [42].…”

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

“…For example, del-Ama et al managed to change muscle stimulation configuration if muscle fatigue is detected -19% drop of torque-time integral in one step [137]. And Sharma et al scaled the desired muscle activation value by the inverse of normalized fatigue estimation to maintain the desired output [153].…”