Kondo Kazuma, Hasegawa Tatsuhito
Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan.
Sensors (Basel). 2021 Nov 21;21(22):7743. doi: 10.3390/s21227743.
In sensor-based human activity recognition, many methods based on convolutional neural networks (CNNs) have been proposed. In the typical CNN-based activity recognition model, each class is treated independently of others. However, actual activity classes often have hierarchical relationships. It is important to consider an activity recognition model that uses the hierarchical relationship among classes to improve recognition performance. In image recognition, branch CNNs (B-CNNs) have been proposed for classification using class hierarchies. B-CNNs can easily perform classification using hand-crafted class hierarchies, but it is difficult to manually design an appropriate class hierarchy when the number of classes is large or there is little prior knowledge. Therefore, in our study, we propose a class hierarchy-adaptive B-CNN, which adds a method to the B-CNN for automatically constructing class hierarchies. Our method constructs the class hierarchy from training data automatically to effectively train the B-CNN without prior knowledge. We evaluated our method on several benchmark datasets for activity recognition. As a result, our method outperformed standard CNN models without considering the hierarchical relationship among classes. In addition, we confirmed that our method has performance comparable to a B-CNN model with a class hierarchy based on human prior knowledge.
在基于传感器的人类活动识别中，已经提出了许多基于卷积神经网络（CNN）的方法。在典型的基于CNN的活动识别模型中，每个类别都被独立对待。然而，实际的活动类别通常具有层次关系。考虑一个利用类别之间的层次关系来提高识别性能的活动识别模型是很重要的。在图像识别中，已经提出了分支卷积神经网络（B-CNN）用于利用类别层次进行分类。B-CNN可以很容易地使用手工制作的类别层次进行分类，但当类别数量很大或先验知识很少时，很难手动设计合适的类别层次。因此，在我们的研究中，我们提出了一种类别层次自适应B-CNN，它在B-CNN中添加了一种自动构建类别层次的方法。我们的方法从训练数据中自动构建类别层次，以便在没有先验知识的情况下有效地训练B-CNN。我们在几个用于活动识别的基准数据集上评估了我们的方法。结果，我们的方法优于不考虑类别之间层次关系的标准CNN模型。此外，我们证实我们的方法具有与基于人类先验知识的类别层次的B-CNN模型相当的性能。
