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Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Jin Zhang
  • Fuxiang Wu
  • Bo Wei
  • Qieshi Zhang
  • Hui Huang
  • Syed W Shah
  • Jun Cheng
<mark>Journal publication date</mark>15/03/2021
<mark>Journal</mark>IEEE Internet of Things Journal
Issue number6
Number of pages14
Pages (from-to)4628-4641
Publication StatusPublished
Early online date25/09/20
<mark>Original language</mark>English


Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.