Gesture recognition has significant applications such as assisted living, e-health, and human-device interactions. Moving away from conventional computer vision techniques, recent studies are turning towards ubiquitous methods such as Wi-Fi and acoustic signals, which offer device deployment at minimal costs. In this paper, we explore these two ubiquitous techniques to enhance gesture recognition, addressing challenges related to multi-modal fusion. Due to the inherent differences in signal types, we employ a tailored method to harmonize information from these distinct signals. Traditional multi-modal fusion methods often lack theoretical models due to insufficient analysis of the foundational characteristics of different signals. In particular, we propose a concept, Hybrid Zone, which is a theoretical model illustrating the fusion of acoustic and Wi-Fi sensing. Hybrid zone offers a comprehensive perspective on the fusion of acoustic and Wi-Fi sensing areas. Moreover, it provides an intricate view of the synthesis of acoustic and Wi-Fi velocities at a granular level. Our experimental results have been promising, achieving a high accuracy rate of 93.75% in gesture recognition.