Occlusion-Corrected EMG-Based Effort Estimator for FES-Assisted Rehabilitation Applications

Voluntary effort combined with functional electrical stimulation (FES) can improve post-stroke motor recovery, yet it is important for the user to actively engage in the rehabilitation task rather than reduce effort during FES assistance, which is an undesired phenomenon known as 'slacking'. To prevent slacking, EMG (electromyography)-controlled FES devices provide assistance as a function of voluntary paretic limb effort that is estimated from paretic muscle EMG using filters that remove stimulation-related responses (m-Waves). However, existing effort estimation methods do not compensate for the reduction of paretic muscle EMG due to interaction of voluntary action potentials with stimulus generated action potentials in the same motor axons, known as 'occlusion'. This phenomenon can lead to undesirable underestimation of voluntary effort during FES applications. In this study, we recruited five people without stroke to investigate the hypothesis that occlusion can be measured using a novel finger extension force tracking task and be used to improve voluntary effort estimation. We evaluated 1) the accuracy of voluntary effort estimation with and without occlusion correction and 2) characterized the effect of different m-Wave filters (Gram-Schmidt and comb filters) on effort estimation accuracy. Results indicated that occlusion correction can significantly improve accuracy of voluntary effort estimated from EMG and that the choice of m-Wave filters can significantly affect effort estimation accuracy.Clinical relevance- This study aims to improve EMG-based effort estimation of functional electric stimulation (FES) rehabilitation participants after stroke. This is critical for 'slacking' reducing FES applications, which have a potential to enhance the efficacy of FES therapy.