The growing physical (PHY) layer capabilities of Wi-Fi have made it possible to use Wi-Fi signals for both communication and human sensing. Wi-Fi channel state information (CSI) in PHY layer can be obtained from commodity Wi-Fi devices. As CSI can detect the minute environment changes that alter signal propagation, it is thus capable of capturing the subtle human activities to provide cost-effective and easy-to-use human sensing. However, the limited bandwidth of each individual Wi-Fi channel fundamentally constrains the resolution of signals, resulting in poor performance of human sensing. In this paper, we present WiRIM, a resolution improving mechanism for Wi-Fi based human sensing. We design a channel switching and aggregation algorithm to extend the effective bandwidth of commodity Wi-Fi signals and improve the performance and efficiency of human sensing applications. With aggregated CSI, WiRIM constructs feature images which contain rich frequency, temporal and spatial characteristics, and then uses a deep learning method to process the extracted features. We propose a cross-location human activity recognition (CLHAR) scenario as a case study. The CLHAR scenario requires a high enough resolution of the Wi-Fi signals to accurately recognize different activities under the interference of tiny changes in human location. The experiments demonstrate the generality and effectiveness of the proposed mechanism.

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