Revolutionizing motor dysfunction treatment: A novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control.

• In order to realize that the electrical stimulation parameters can be adjusted in real time according to the amount of feedback, a predictive control algorithm based on the Hammerstein model for closed-loop feedback electrical stimulation is investigated. • A Hammerstein model of the stimulated muscle group is established, and a recursive least-square identification algorithm is used to identify online parameters of the Hammerstein model with muscle time-varying. • The experiments show that the model parameters are well identified, with a relative RMS error of 3.83 % between actual and predicted values. • The predictive control algorithm based on the motor tasks is able to adaptively adjust the stimulus parameters so that the stimulated muscle groups achieve the desired sEMG characteristic trajectory. Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model. [ABSTRACT FROM AUTHOR]