FinerSense: A Fine-Grained Respiration Sensing System Based on Precise Separation of Wi-Fi Signals

This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component's amplitude for precise separation. A system called FinerSense is developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.