DGRU based human activity recognition using channel state information

•Devices free non-obtrusive and privacy preserved human activity recognition.•Deep Learning approach for activity classification using Channel State Information.•Human activity dataset collection by involving 30 volunteers.•Deep Gated Recurrent Unit outperforms traditional activity classification sc...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2021-01, Vol.167, p.108245, Article 108245
Main Authors: Bokhari, Syed Mohsin, Sohaib, Sarmad, Khan, Ahsan Raza, Shafi, Muhammad, Khan, Atta ur Rehman
Format: Article
Language:English
Subjects:
Channel state information
Data collection
Deep learning
DGRU
Discrete Wavelet Transform
Discriminant analysis
Empirical analysis
Feature extraction
Human activity recognition
Indoor environments
LSTM
Machine learning
Moving object recognition
Neural networks
Passive monitoring
Performance evaluation
Random forest
Software
Software radio
Wavelet transforms
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Summary:•Devices free non-obtrusive and privacy preserved human activity recognition.•Deep Learning approach for activity classification using Channel State Information.•Human activity dataset collection by involving 30 volunteers.•Deep Gated Recurrent Unit outperforms traditional activity classification schemes. In this work, we have proposed a Deep Gated Recurrent Unit (DGRU) model for non-obtrusive human activity recognition using Channel State Information (CSI). Empirical model decomposition is used for de-noising, whereas discrete wavelet transforms and linear discriminant analysis are used for feature extraction and dimensionality reduction, respectively. For extensive experimental evaluation and comparative analysis, a Software Defined Radio (SDR) platform is used by implementing IEEE 802.11a on National Instruments’ Universal Software Radio Peripheral (USRP). The physical layer CSI is collected in an indoor environment to evaluate the performance for seven activities. 30 volunteers including both genders and of different age groups were involved in the data collection process. As demonstrated through experiments, the proposed scheme achieves promising results with an accuracy of 95–99% for all activities, outperforming the traditional benchmark approaches in the literature that use random forest and more advanced deep learning techniques, such as Long-Short Term Memory (LSTM).
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.108245