Device-Free Wireless Sensing With Few Labels Through Mutual Information Maximization.

Empowered by the feature extraction ability of deep neural networks (DNNs), the DNN-based device-free wireless sensing (DFWS) could recognize human activity by analyzing the pattern information involved in the influenced wireless signals. However, labeling samples is time-consuming and labor-intensive because wireless signals are not human-interpretable. In practical applications, there are always few labeled samples, and how to realize high-performance DFWS with few labels becomes an urgent problem to solve. To tackle this problem, finding compact representative features for samples in an unsupervised manner is crucial. To this end, we design a contrastive learning framework to obtain features of unlabeled samples by maximizing the mutual information between features and the corresponding samples. The contrastive training process extracts features for the input samples by contrasting positive and negative sample pairs, thus strengthening the correlation between the features and the corresponding samples. The intuition behind our method is that mutual information measures the correlation between features and samples, and thus the maximum mutual information could capture informative and discriminative features. Our evaluation results on two publicly available datasets and one dataset collected by ourselves show that our proposed method achieves satisfactory accuracy for both human activity and gesture recognition tasks with few labels.