Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes
This work provides an incremental improvement for activity recognition in smart homes, which is important for developing automatic services for inhabitants.
The paper addresses activity recognition in smart homes by combining Natural Language Processing (NLP) and Time Series Classification (TSC) methods. The proposed method, which uses a Fully Convolutional Network (FCN) bootstrapped by word encoding and embedding, demonstrates good performance in offline activity classification on two CASAS datasets.
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.