Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

Due to the high-fidelity mapping of the physical buildings and the intelligent performance shown in their lifecycle, digital twins (DTs) have gained increasing attention in the building sector. Although digital twins based on building information modeling (BIM) have become a hot research topic, existing works emphasize the digitization of building static and dynamic information and lack a unified consideration of the inherent physical mechanisms and interactive behaviors of buildings. To this end, this paper proposes a foundation model for building digital twins which realizes the unification of building static information, physical mechanisms and interaction patterns. The conceptual framework of the model is given first and then formal modeling and verification with time automata theory are performed to demonstrate the plausibility of the model. Finally, a practical digital twin of a chiller is developed based on the proposed foundation model as an example, thus, indicating its effectiveness and credibility.