Handwritten signature verification using a wearable surface-EMG armband

The growing demand for remote authentication underscores the importance of robust signature verification systems. A major challenge in this domain is the substantial intra-class variability inherent in handwritten signatures. This study investigates the use of surface electromyography (sEMG) for signature verification through wearable armbands, aiming to address this issue. We introduce a dual-model deep learning framework that integrates muscle co-activation patterns with raw sEMG signal waveforms. A 4-channel armband was employed to collect sEMG data from 20 individuals signing Chinese characters, resulting in the first sEMG signature dataset centered on wearable acquisition. Experimental results show that conventional feature-based machine learning methods are limited in performance, yielding 80.90% accuracy and a 12.82% equal error rate (EER), primarily due to high intra-class variability. The proposed framework comprises: (1) a CNN-LSTM architecture that processes encoded muscle activation sequences, and (2) a multi-branch CNN designed to learn from raw sEMG signals. Fusion at the decision level between these models achieves 91.65% accuracy and 5.25% EER, reflecting a 10.75% improvement in accuracy compared with traditional techniques. These findings confirm the effectiveness of the proposed approach in reducing intra-class variability while preserving the usability of wearable devices, offering a practical and secure biometric authentication solution.