Sense+: A Plug-and-Play Signal Preprocessing Approach for Enhancing Human-Centered Wireless Sensing

Human-centered wireless sensing has been significantly advanced by artificial intelligence (AI) technologies. To enhance AI model performance, signal preprocessing, as a fundamental procedure, is widely employed for improving signal quality. However, existing methods are time-consuming, labor-intensive, and exhibit limited generalization. To address this issue, we first investigate the frequency spectrum of various signals. The results demonstrate that, in human-centered wireless applications, human activities significantly affect the low-frequency components in the signal spectrum. Motivated by this observation, we propose Sense+, a concise and versatile signal preprocessing module that can be seamlessly integrated into existing models to enhance sensing performance. Specifically, we transform the raw signals into a unified frequency domain, apply a learnable filter to process their frequency spectra, and then convert them back to the original signal domain. To accurately extract low-frequency features, we further propose a low-pass weight initialization method for the filter. Extensive experiments are conducted across various sensing tasks and signal types, including IR-UWB signals for person identification, mmWave radar signals for gesture recognition, and Wi-Fi signals for action recognition. The results highlight the effectiveness of Sense+ in enabling preprocessing across diverse wireless signals. Specifically, when equipped with Sense+, the average accuracy improves by 21.84% compared to conventional preprocessing methods. Additionally, Sense+ accelerates convergence and exhibits consistent generalization across different neural network models.