  Low-resolution infrared-based human activity recognition (HAR) attracted
enormous interests due to its low-cost and private. In this paper, a novel
semi-supervised crossdomain neural network (SCDNN) based on 8 $\times$ 8
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.
