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

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.

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 models, including SVM, LightGBM, and MLP, were employed for gesture classification.

Most models achieved over 90% precision on both cross-validation and test sets, demonstrating robust classification performance across different gesture types and subject conditions.

These results confirm that the proposed system maintains high recognition accuracy even in severely impaired subjects. The dataset presented here offers substantial value for gesture recognition research, rehabilitation assessment, and neuromuscular signal analysis.