论文标题
完全卷积的网络通过单词编码和嵌入智能家居中的活动识别的单词进行引导
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes
论文作者
论文摘要
当我们希望为居民提供自动服务时，智能家居中的活动认可至关重要。但是，它在环境的可变性，感觉运动系统以及用户习惯方面构成了挑战。因此，终端系统在自动提取关键功能的情况下失败，而无需大量的预处理。我们建议通过合并自然语言处理（NLP）和时间序列分类（TSC）域中的方法来解决智能家居活动的功能提取。我们在自适应系统高级研究中心（CASA）发行的两个数据集上评估了我们的方法的性能。此外，我们分析了使用NLP编码袋与嵌入式编码袋的贡献，以及FCN算法自动提取功能和分类的能力。我们提出的方法在离线活动分类中表现出良好的性能。我们的分析还表明，FCN是用于智能家庭活动识别的合适算法，并且是自动特征提取的优势。
Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it poses challenges in terms of variability of the environment, sensorimotor system, but also user habits. Therefore, endto-end systems fail at automatically extracting key features, without extensive pre-processing. We propose to tackle feature extraction for activity recognition in smart homes by merging methods from the Natural Language Processing (NLP) and the Time Series Classification (TSC) domains. We evaluate the performance of our method on two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). Moreover, we analyze the contributions of the use of NLP encoding Bag-Of-Word with Embedding as well as the ability of the FCN algorithm to automatically extract features and classify. The method we propose shows good performance in offline activity classification. Our analysis also shows that FCN is a suitable algorithm for smart home activity recognition and hightlights the advantages of automatic feature extraction.
