Assisting Hand Gesture Classification and Rehabilitation Assessment via sEMG and Finger Motion Data

Xiu xiu Yang¹Ling feng Zhang^1,2Fu kui Wu^1,3Xin ran Wei¹Hai feng Huang¹Jun Li^1,4*Tao Hu^1,3

• ¹Intelligent Science and Engineering, Hubei Minzu University, Hubei Enshi, China
• ²Tokyo Daigaku, Bunkyo, Japan
• ³Hubei Engineering Research Center of Selenium Food Nutrition and Health Intelligent Technology, Enshi, China
• ⁴Hubei Minzu University, Sports Health and Collaborative Intelligent Engineering Research Center, Enshi, Hubei, China

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 both clinical relevance and independence of the hardware–database framework. The recorded signals were processed using a Savitzky – Golay filter to retain high-frequency features, followed by Short-Time Fourier Transform (STFT) for spectrogram generation. Multiple machine learning models, including SVM, LightGBM, and MLP, were employed for gesture classification, with most achieving over 90% precision on both cross-validation and test sets. 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.