Meta-Learning for Human-Centered Wireless Sensing: Architecture, Applications, and Challenges

Meng Xue, Yanjiao Chen, Xueluan Gong, Houze Cao, Jian Zhang, Qian Zhang

Abstract: With the advancement of mobile sensing and artificial intelligence technologies, human-centered wireless sensing methodologies have shown the great potential of using wireless signals (e.g., acoustic, WiFi, radar) to realize contactless and non-intrusive services. These applications facilitate various domains for smart homes and smart cities, such as security, medication, and transportation. However, current human-centered wireless sensing systems require complex signal preprocessing, handcrafted feature extraction, and a tremendous number of labeled data, which significantly hinder its performance and adoption. In this article, we present a meta-learning-based human-centered wireless sensing framework, which optimizes signal preprocessing, feature extraction, and data analysis by utilizing meta-knowledge learned in meta-learning. Specifically, we present the working principle, architecture, potential applications, research challenges and future directions of meta-learning-based human-centered wireless sensing.