Affective Communication: Designing Semantic Communication for Affective Computing
Chia‐Han Lee, Po-Hsiang Huang, Tsung‐Han Lee, Po‐Hao Chen · 2024
Affective computing is an active area of research, but how to efficiently transmit the sensed data to the server for affective computing is less investigated. In this paper, we propose affective communication for affective computing, with the wireless link from the affection-sensing devices to the affective-computing server being semantic communication. The semantic communication problem asks how precisely the transmitted symbols convey the desired meaning, and thus the semantic communication for affective computing is the most efficient if the meaning of the sensed affection data is conveyed for affective computing with minimum wireless resources used. Deep neural networks (DNNs) are adopted as the semantic-channel encoder and the semantic-channel decoder for end-to-end joint design. Simulations using the FER2013 facial expression recognition dataset shows the effectiveness of the proposed DNN-based semantic communication codec in affective communication for affective computing. Furthermore, federated learning for affective communication is investigated for privacy concerns.