| Autor: |
Fratti R; Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland., Marini N; Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland., Atzori M; Department of Neuroscience, University of Padua, 35122 Padua, Italy., Müller H; Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland.; Medical Informatics, University of Geneva, 1205 Geneva, Switzerland.; The Sense Innovation and Research Center, 1007 Lausanne, Switzerland., Tiengo C; Department of Neuroscience, University of Padua, 35122 Padua, Italy.; Clinic of Plastic Surgery, University Hospital of Padua, 35128 Padova, Italy., Bassetto F; Department of Neuroscience, University of Padua, 35122 Padua, Italy.; Clinic of Plastic Surgery, University Hospital of Padua, 35128 Padova, Italy. |
| Abstrakt: |
Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance. |
