Acoustic Sensing for Contactless Health Monitoring: Technologies, Algorithms, and Emerging Applications

Abstract

Contactless acoustic sensing is emerging as a potential solution for health monitoring due to its ubiquity, low cost, and non-invasive nature. This paper presents a unified framework for acoustic-based vital sign detection, encompassing signal transmission, reception, processing, and algorithmic inference. Firstly, a structured taxonomy is proposed that categorizes existing methods into active sensing, passive sensing, and multimodal fusion systems. Secondly, special attention is given to recent advancements in signal processing and machine learning, including deep learning models and time–frequency analysis techniques that enhance monitoring accuracy under real-world conditions. Thirdly, this paper explores practical deployment scenarios and highlight key challenges such as noise resilience, multi-user interference, and privacy concerns. By integrating insights across domains, this review uniquely positions acoustic sensing within the broader context of scalable, privacy-conscious, and robust health monitoring.