A deep learning framework for finger motion recognition using forearm ultrasound imaging.

ABSTRACT: Precise classification of finger movements is essential for effective hand gesture recognition, as it facilitates reliable control across multiple degrees of freedom. Surface electromyography (sEMG) has been widely used for monitoring muscle activities in the forearm and applied for the classification of finger motion. However, due to its inherent limitations, sEMG fails to provide a satisfactory solution for finger motion recognition. As an alternative, A-mode ultrasound-based sensing methods have been proposed and have demonstrated great potential. In this paper, we present a finger motion classification method based on deep learning using forearm ultrasound imaging. Since B-mode ultrasound offers 2D visualization of muscle activity, it capture broader anatomical features and reducing se

SUBMITTER: Lee Y 

PROVIDER: S-EPMC12612224 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature