WiLAR: A Location-adapted Action Recognition System based on WiFi

In modern society, wireless signals are ubiquitous in various indoor environments, such as living houses, offices and shop malls, facilitating human living in various aspects. Action recognition is a technique in roaring demand in the field of human-computer interaction. Whereas previous research studies propose various methods to action recognition using wireless signals, action recognition in locations with limited data is still very challengeable. To realize decent action recognition with the help of wireless signals, we propose WiLAR, a location-adapted action recognition system based on WiFi, which enables action detection, segmentation and recognition with commodity WiFi devices in locations with different amounts of data. WiLAR extracts informative features from fine-grained WiFi channel state information (CSI), and then feeds features into elaborately designed deep learning models to realize action recognition in different locations. In our dedicated experiments, WiLAR achieves average 97% accuracy workout recognition in locations with plenty of data, and also outperforms other recognition models in locations with limited training data.