Assisting hand gesture classification and rehabilitation assessment via sEMG and finger motion data.

ABSTRACT:

Introduction

To address the lack of integrated and clinically applicable motion capture systems for hand function assessment, we developed a wearable device capable of simultaneously recording finger curvature and surface electromyography (sEMG) signals from both healthy individuals and patients with motor impairments.

Methods

The dataset comprises 900 measurements of six predefined gestures collected from 15 participants using a six-channel sEMG motion-capture glove. Data were obtained through hospital-based field acquisition, ensuring clinical relevance and independence of the hardware-database framework. The recorded signals were processed using a Savitzky-Golay filter, followed by Short-Time Fourier Transform (STFT) for spectrogram generation. Multiple machine learning m

SUBMITTER: Yang X 

PROVIDER: S-EPMC12823985 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature