Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning

Deep Learning plays an increasingly important role in device-free WiFi Sensing for human activity recognition (HAR). Despite its strong potential, significant challenges exist and are associated with the fact that one may require a large amount of samples for training, and the trained network cannot be easily adapted to a new environment. To address these challenges, we develop a novel scheme using matching network with enhanced channel state information (MatNet-eCSI) to facilitate one-shot learning HAR. We propose a CSI correlation feature extraction (CCFE) method to improve and condense the activity-related information in input signals. It can also significantly reduce the computational complexity by decreasing the dimensions of input signals. We also propose novel training strategy which effectively utilizes the data set from the previously seen environments (PSE). In the least, the strategy can effectively realize human activity recognition using only one sample for each activity from the testing environment and the data set from one PSE. Numerous experiments are conducted and the results demonstrate that our proposed scheme significantly outperforms state-of-the-art HAR methods, achieving higher recognition accuracy and less training time.

Automated summary

Deep Learning plays a crucial role in device-free WiFi Sensing for human activity recognition. However, challenges such as the need for a large amount of training samples and network adaptation to new environments still exist. To address these challenges, we propose a novel scheme using matching network with enhanced channel state information (MatNet-eCSI) for one-shot learning HAR. Our proposed scheme improves and condenses activity-related information in input signals, significantly reducing computational complexity. It also utilizes data from previously seen environments (PSE) for effective training. Experimental results show that our scheme outperforms state-of-the-art HAR methods, achieving higher recognition accuracy and less training time.