With the development of the Internet of Things (IoT) and the popularization of commercial WiFi, researchers have begun to use commercial WiFi for human activity recognition in the past decade. However, cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes. To solve this problem, we try to build a cross-scene activity recognition system based on commercial WiFi. Firstly, we use commercial WiFi devices to collect channel state information (CSI) data and use the Bi-directional long short-termmemory (BiLSTM) network to train the activity recognition model. Then, we use the transfer learning mechanism to transfer the model to fit another scene. Finally, we conduct experiments to evaluate the performance of our system, and the experimental results verify the accuracy and robustness of our proposed system. For the source scene, the accuracy of the model trained from scratch can achieve over 90%. After transfer learning, the accuracy of cross-scene activity recognition in the target scene can still reach 90%.
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