论文标题
使用深胶囊网络和游戏理论，可穿戴IMU的连续手语识别
Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory
论文作者
论文摘要
聋人社区在全球范围内使用了手语。这里提出的工作提出了一种新颖的一维深胶囊网络（CAPSNET）架构，以通过从定制设计的可穿戴IMU系统获得的信号来进行连续的印度手语识别。通过更改胶囊层之间的动态路由来评估所提出的CAPSNET架构的性能。与卷积神经网络（CNN）相比，提出的CAPSNET在3个路由的5个路由的精度值为94％，而5个路由的精度为92.50％，该卷积神经网络（CNN）的精度为87.99％。通过描述预测层激发单元的空间激活，还可以验证对所提出架构的改进学习。最后，在Capsnet和CNN之间设计了一项新型的非合作选择比赛。与CNN相比，CAPSNET的NASH平衡值较高，表明该方法的适用性。
Sign Language is used by the deaf community all over world. The work presented here proposes a novel one-dimensional deep capsule network (CapsNet) architecture for continuous Indian Sign Language recognition by means of signals obtained from a custom designed wearable IMU system. The performance of the proposed CapsNet architecture is assessed by altering dynamic routing between capsule layers. The proposed CapsNet yields improved accuracy values of 94% for 3 routings and 92.50% for 5 routings in comparison with the convolutional neural network (CNN) that yields an accuracy of 87.99%. Improved learning of the proposed architecture is also validated by spatial activations depicting excited units at the predictive layer. Finally, a novel non-cooperative pick-and-predict competition is designed between CapsNet and CNN. Higher value of Nash equilibrium for CapsNet as compared to CNN indicates the suitability of the proposed approach.
