Framework for Learning a Hand Intent Recognition Model from sEMG for FES-Based control

Stroke survivors and individuals with neuromus-cular disorders often experience motor function impairments, particularly during hand movements crucial for activities of daily living (ADL). Functional Electrical Stimulation (FES) has emerged as a potential assistive and rehabilitative technique to address these limitations. However, accurately determining user intent during FES poses a significant challenge. This work proposes a framework for rapidly learning a model of the user's hand intent from surface electromyography (sEMG) signals, specifically for continuous FES-based control of the ipsilateral hand. The framework systematically collects data from expected volitional and FES-evoked hand motions, followed by training a logistic regression model for intent classification. The study demonstrates that the proposed model can learn from limited data and compares favorably to deep neural nets trained on the same dataset. This model is able to recognize user intent with high accuracy even during concurrent FES stimulation.