WISDOM: Wi-Fi Based Contactless Multi-User Activity Recognition

Pengsong Duan, Chen Li, Jie Li, Xianfu Chen, Chao Wang, Endong Wang

Research output: Contribution to journalArticleScientificpeer-review


Wi-Fi-based contactless activity recognition is of great importance to computer-human interaction, accounting for convenience concerns. However, it remains challenging to recognize activities from multiple users due to the multi-path distortion and disruption of Wi-Fi signals. In this paper, we propose a highly universal framework, namely WISDOM, for Wi-Fi based multi-user activity recognition. Specifically, we first leverage an existing model to identify the number of users from the input Wi-Fi signals. Then, we develop a sub-carrier correlation and inversion based sorting algorithm to extract the signal for each user. Finally, we design a neural network, i.e., WISDOM-Net, which is built on a bidirectional gated recurrent unit network incorporated with the attention mechanism and the one dimension convolutional neural network, to recognize the corresponding user activities. Experimental results show that our proposed WISDOM-Net outperforms the existing baselines on both the public and our own datasets. In particular, WISDOM-Net can reach an average recognition accuracy of up to 98.19% and 90.77% in 2-user and 3-user scenarios, respectively.

Original languageEnglish
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2022
MoE publication typeA1 Journal article-refereed


  • Activity recognition
  • blind signal separation
  • channel state information
  • Feature extraction
  • Internet of Things
  • Monitoring
  • Multi-user activity recognition
  • object sensing
  • Receivers
  • Transmitters
  • Wi-Fi
  • Wireless fidelity


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