Wi-Limb: Recognizing Moving Body Limbs Using a Single WiFi Link

Utilizing fine grained analysis of wireless signals for human activity recognition has gained a lot of traction recently. The unique changes to the ambient wireless signals caused by different activities made it possible to recognize these fingerprints through deep learning classification methods. Most of the existing work consider a set of physical activities or gestures and try to recognize each one of them as a separate class. However, this makes the classification task harder especially when the number of activities to recognize becomes larger and when these activities include movements from the same body parts. To address that, in this study, we consider the decomposition of each physical activity into the limbs and body parts involved in that activity and study a one-by-one recognition solution. We propose a Generative Adversarial Network (GAN)-based hierarchical method that not only recognizes the involved body limbs and facilitates the recognition of complex activities, but also mitigates the temporal effects in the collected signal data and thus provides a generalized solution. Our experimental evaluation shows that we can recognize unknown physical activities through the proposed hierarchical limb recognition based model with a small Hamming loss and by just using WiFi signal data from a single transmitter and receiver link.