论文标题
基于SEMG的手势分类的转移学习使用大师奴隶体系结构中的深度学习
Transfer Learning for sEMG-based Hand Gesture Classification using Deep Learning in a Master-Slave Architecture
论文作者
论文摘要
基于手势的人体机器界面的诊断学习和开发的最新进展使表面肌电图（SEMG）朝着重要的重要性驱动。手势分析需要对SEMG信号进行准确的评估。拟议的工作提出了一种新型的顺序主奴隶体系结构，该结构由深层神经网络（DNN）组成，用于使用来自多个SEMG频道记录的信号来分类印度手语的标志。通过利用长期短期内存网络生成的其他合成功能数据来增强主奴隶网络的性能。在添加合成数据之前和之后，将提出的网络的性能与常规DNN的性能进行了比较。在常规DNN中观察到高达14％的改善，并且在添加合成数据的平均准确性值为93.5％，主张提出的方法的适用性，在主奴隶网络中最多提高了9％。
Recent advancements in diagnostic learning and development of gesture-based human machine interfaces have driven surface electromyography (sEMG) towards significant importance. Analysis of hand gestures requires an accurate assessment of sEMG signals. The proposed work presents a novel sequential master-slave architecture consisting of deep neural networks (DNNs) for classification of signs from the Indian sign language using signals recorded from multiple sEMG channels. The performance of the master-slave network is augmented by leveraging additional synthetic feature data generated by long short term memory networks. Performance of the proposed network is compared to that of a conventional DNN prior to and after the addition of synthetic data. Up to 14% improvement is observed in the conventional DNN and up to 9% improvement in master-slave network on addition of synthetic data with an average accuracy value of 93.5% asserting the suitability of the proposed approach.
