Abstract
In this demo, we introduce a hands-free human activity recognition framework leveraging millimeter-wave (mmWave) sensors. Compared to other existing approaches, our network protects user privacy and can remodel a human skeleton performing the activity. Moreover, we show that our network can be achieved in one architecture, and be further optimized to have higher accuracy than those that can only get singular results (i.e. only get pose estimation or activity recognition). To demonstrate the practicality and robustness of our model, we will demonstrate our model in different settings (i.e. facing different backgrounds) and effectively show the accuracy of our network.