Ahmed W. Shehata

This paper introduces a new set of evaluation metrics for myoelectric prosthesis controllers and demonstrates their use in comparing deep learning and traditional controllers.

This paper develops and tests RCNN-based controllers for myoelectric prostheses to mitigate the limb position effect, showing improved performance over traditional methods in multiple metrics.

This paper introduces a method to reduce bias in estimating trial-by-trial motor adaptation during natural movements, improving accuracy compared to conventional approaches.