Activity Recognition and Classification via Deep Neural Networks

  • Zhi Wang
  • Liangliang Lin
  • Ruimeng Wang
  • Boyang Wei
  • Yueshen Xu
  • Zhiping Jiang
  • Rui LiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 309)


Based on the Wi-Fi widely separated in the world, Wi-Fi-based wireless activity recognition has attracted more and more research efforts. Now, device-based activity awareness is being used for commercial purpose as the most important solution. Such devices based on various acceleration sensors and direction sensor are very mature at present. With more and more profound understanding of wireless signals, commercial wireless routers are used to obtain signal information of the physical layer: channel state information (CSI) more granular than the RSSI signal information provides a theoretical basis for wireless signal perception. Through research on activity recognition techniques based on CSI of wireless signal and deep learning, the authors proposed a system for learning classification using deep learning, mainly including a data preprocessing stage, an activity detection stage, a learning stage and a classification stage. During the activity detection model stage, a correlation-based model was used to detect the time of the activity occurrence and the activity time interval, thus solving the problem that the waveform changes due to variable environment at stable time. During the activity recognition stage, the network was studied by innovative deep learning to conduct training for activity learning. By replacing the fingerprint way, which is used broadly today, with learning the CSI signal information of activities, we classified the activities through trained network.


Channel state information Pearson correlation coefficient Deep convolutional neural networks AlexNet network 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Zhi Wang
    • 1
  • Liangliang Lin
    • 2
    • 3
  • Ruimeng Wang
    • 4
  • Boyang Wei
    • 5
  • Yueshen Xu
    • 6
  • Zhiping Jiang
    • 6
  • Rui Li
    • 6
    Email author
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Computer Science and TechnologyXi’an Jiaotong UniversityXi’anChina
  3. 3.Informatization Office, Xi’an Conservatory of MusicXi’anChina
  4. 4.School of Photovoltaic and Renewable Energy EngineeringThe University of New South WalesSydneyAustralia
  5. 5.Geogetown UniversityWashington DCUSA
  6. 6.School of Computer Science and TechnologyXidian UniversityXi’anChina

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