Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

TL;DR

This paper introduces a novel approach combining NLP encoding and Fully Convolutional Networks to improve automatic feature extraction and activity recognition accuracy in smart home environments.

Contribution

It presents a new method merging NLP techniques with FCN for activity recognition, demonstrating effective automatic feature extraction and classification in smart homes.

Findings

FCN effectively extracts features automatically

NLP encoding improves activity classification

Method shows good offline performance on CASAS datasets

Abstract

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.