WIFIACT: Enhancing Human Sensing Through Environment Robust Preprocessing And Bayesian Self-Supervised Learning

Abstract

Wi-Fi Sensing is emerging as a transformative paradigm in the realm of smart environments, enabling the ubiquitous detection of human presence and the identification of activities within indoor spaces. This paper presents WiFiAct, which leverages a 20 MHz 1 transmit 1 receive (1T1R) Wi-Fi monitor to achieve state-of-the-art generalization results. Our methodology capitalizes on a custom preprocessing pipeline and harnesses the power of self-supervised learning frame-work utilizing a Bayesian Convolutional Neural network (BCNN) and novel contrastive augmentation techniques. Our custom pre-processing pipeline is capable of extracting environment invariant features from Wi-Fi signals, enhancing the expressive power of the subsequent classifier. The proposed self-supervised methodology utilizes a combination of unlabeled and labeled data to effectively learn representations that enable accurate geofenced activity recognition amid uncertainties. Through extensive experimentation, we showcase the proposed solution’s generalization capabilities, paving the way for innovative applications in presence detection and activity identification within smart environments.