Model-Based Control of FES Embedding Simultaneous Volitional EMG Measurement

Abstract

There are over one million people in the UK with upper limb impairment following stroke. Artificial activation of muscle can be achieved using functional electrical stimulation (FES), and enable recovery by facilitating task practice. Signicant clinical research supports the utility of FES for both orthotic and therapeutic purposes, and shows that the effectiveness is maximised when applied concurrently with a patient's voluntary effort. Voluntary effort can be captured using electromyography (EMG), however existing FES control schemes using EMG are predominantly open-loop and fail to provide accurate assistance. In this paper, a model of the dynamic interaction between voluntary and evoked muscle activation is developed, embedding both nonlinear recruitment and activation dynamics. Then an identification method is proposed suitable for clinical application. This enables a model-based, hybrid EMG/FES control scheme to be developed, allowing the dual objectives of tracking and volitional intention support to be optimized for the first time. Experimental results show that the tracking performance of the controller is far more effective compared to previous FES approaches which neglect voluntary action.