Reference : Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal
Scientific journals : Article
Engineering, computing & technology : Computer science
Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal
Zhang, Jin []
Wu, Fuxiang Wu []
Wei, Bo []
Zhang, Qieshi []
Huang, Hui mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
Shah, Syed W. []
Cheng, Jun []
IEEE Internet of Things Journal
[en] WiFi ; channel state information ; human activity recognition ; data augmentation ; neural network
[en] 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 wireless signal propagation, that manifested as unique patterns for activities 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 carry 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 certain speed and scale, recent works commonly have 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 synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep learning model that cater 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.

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