论文标题
弧网：通过胶囊识别活动
ARC-Net: Activity Recognition Through Capsules
论文作者
论文摘要
人类活动认可（HAR）是一个具有挑战性的问题，需要先进的解决方案，而不是使用手工制作的功能来实现理想的性能。已经提出了深度学习作为解决方案，以获得更准确的HAR系统，以抵抗噪声。在本文中，我们介绍了ARC-NET，并提出了胶囊的利用来融合来自多个惯性测量单元（IMU）的信息，以预测受试者执行的活动。我们假设该网络将能够收集不必要的信息，并能够通过嵌入胶囊网络中的迭代机制做出更准确的决策。我们提供网络学到的先验的热图，以可视化训练有素的网络对每个数据源的利用。通过使用拟议的网络，我们能够将最新方法的准确性提高2％。此外，我们研究了结果混乱矩阵的方向性，并根据提供的数据讨论活动的特异性。
Human Activity Recognition (HAR) is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data.
