On the Use of Deeper CNNs in Hand Gesture Recognition Based on sEMG Signals

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)(2019)

引用 12|浏览8
暂无评分
摘要
In the past few years, a great interest for the classification of hand gestures with Deep Learning methods based on surface electromyography (sEMG) signals has been developed in the scientific community. In line with latest works in the field, the objective of our work is the construction of a novel Convolutional Neural Network architecture, for the classification of hand-gestures. Our model, while avoiding overfitting, did not perform significantly better compared to a much shallower network. The results suggest that the lack of diversity in the sEMG recordings between certain hand-gestures limits the performance of the model. In addition, the classification accuracy on a database we developed using a commercial device (Myo Armband) was substantially higher (approximately 24%) than a similar benchmark dataset recorded with the same device.
更多
查看译文
关键词
Deep Learning,surface electromyography,sEMG,Convolutional Neural Networks,CNN,hand gesture recognition,classification,database,data acquisition,signal processing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要