Improving WiFi-based human activity recognition with adaptive initial state via one-shot learning

WiFi-based human activity recognition technology has attracted widespread attention for its prominent application value and theoretical significance. Existing approaches have made great achievements in the same domain sensing, which means the activity samples applied for training the model have a similar distribution with the testing data. However, in practical application, we hope that the same activity of different people with various states and habits in different locations can be accurately recognized and produce the same reaction. Therefore, cross-domain sensing technology is pretty important. Some studies explore the location-independent and environment-independent methods, but few attempts consider the influence of the initial states of the users, such as standing and sitting, which actually have very different effects on the transmission of the wireless signal. This paper presents a human activity recognition method adapted to different initial states. Meanwhile, we solve the accompanying issue of the small sample size sensing, obviating the need for the cumbersome wok resulting from the massive data collection. We take advantage of the idea of metric learning and few-shot learning to realize cross-domain sensing with very few samples. The experiments demonstrate the feasibility and excellent performance of our method, which could recognize human activities with different initial states as the training data.