Demo: Leveraging Edge Intelligence for Affective Communication over URLLC

IoT systems are advancing to enable higher levels of engagement and omnipresence. A critical yet uncharted domain lies in communicating affect across participants, especially in medical settings where emotions and expressions are pivotal, and eXtended Reality (XR) systems that rely on immersive virtualization of the participating parties. However, IoT systems seldom have the bandwidth or reliability to enable such services. In this demo, we present an experiment that leverages Edge Intelligence and Artificial Intelligence to extract and encode emotions at one edge, and communicate a low-footprint encapsulation of such emotions at the other edge. The proposed architecture is designed to reduce overall traffic and build on low-power video and display equipment, to realize Affective Semantic Communication (AffSeC). This demonstration shall represent AffSeC in a medical setting, where a patient interacts with a physician over a low-BW E2E route. The proposed scheme will be contrasted to standard video compression to demonstrate the efficacy and promise of this model.