Department of Applied Science for Integrated System Engineering Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.
Department of Basic Sciences Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.
Sensors (Basel). 2019 Nov 19;19(22):5043. doi: 10.3390/s19225043.
In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset.
在本文中，我们探讨了使用词嵌入进行传感器活动识别的零样本学习。零样本学习的目标是通过学习识别语义向量中表示的活动组件来估计未知的活动类别（即给定训练数据集中不存在的活动）。现有的零样本方法主要使用 2 种表示形式作为语义向量，属性向量和嵌入词向量。然而，基于嵌入向量的零样本活动识别方法研究甚少；据我们所知，特别是对于基于传感器的活动识别，尚无此类研究。在本文中，我们比较和彻底评估了具有不同语义向量的零样本方法：（1）属性向量，（2）嵌入向量和（3）扩展嵌入向量，并分析它们与性能的相关性。我们的结果表明，这三个空间的性能相似，但使用词嵌入可以产生更有效的方法，因为这种类型的语义向量可以自动生成。此外，当给定的传感器数据和语义向量中存在相似信息时，我们建议的方法比属性向量方法实现了更高的准确性；这项研究的结果有助于选择合适的类别和传感器数据来构建训练数据集。
