Human Activity Recognition With Low-Resolution Infrared Array Sensor Using Semi-Supervised Cross-Domain Neural Networks for Indoor Environment
Cunyi Yin, Xiren Miao, Jing Chen, Hao Jiang, Deying Chen, Yixuan Tong, Shaocong Zheng
Abstract
Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low cost and private. In this article, a novel semi-supervised cross-domain neural network (SCDNN) based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low cost. The SCDNN consists of feature extractor, domain discriminator, and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low cost yet highly adaptable solution for cross-domain scenarios.