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Recompositing channel state information (CSI) from the beamforming feedbackmatrix (BFM), which is a compressed version of CSI and can be captured becauseof its lack of encryption, is an alternative way of implementingfirmware-agnostic WiFi sensing. In this study, we propose the use of cameraimages toward the accuracy enhancement of CSI recomposition from BFM. The keymotivation for this vision-aided CSI recomposition is to draw a first-handinsight that the BFM does not fully involve spatial information to recompositeCSI and that this could be compensated by camera images. To leverage the cameraimages, we use multimodal deep learning, where the two modalities, i.e., imagesand BFMs, are integrated to recomposite the CSI. We conducted experiments usingIEEE 802.11ac devices. The experimental results confirmed that therecomposition accuracy of the proposed multimodal framework is improvedcompared to the single-modal framework only using images or BFMs.

Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach