Stage-specific EMG feature optimization for enhanced post-stroke hand gesture recognition.

ABSTRACT:

Background

EMG-based hand-gesture recognition can enable home-based post-stroke rehabilitation, yet one-size-fits-all feature sets overlook differences across recovery stage METHODS: Thirteen post-stroke participants performed seven gestures while EMG was recorded from six forearm sensors. From 38 time- and frequency-domain features, we derived stage-specific subsets for Low (Brunnstrom 1-2, minimal movement), Medium (3-4, partial movement), and High (5-6, near-normal movement) using a wrapper approach Sequential Forward Selection (SFS). For reference, we included a filter comparison using minimum Redundancy-Maximum Relevance (mRMR). To provide fair baselines, we reproduced two literature feature sets within an identical Light Gradient Boosting Machine (LightGBM) pipeline: (i) a he

SUBMITTER: Mansour O 

PROVIDER: S-EPMC12797887 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature