DGRU based human activity recognition using channel state information

• Published in: Measurement : journal of the International Measurement Confederation Vol. 167; p. 108245
• Main Authors: Bokhari, Syed Mohsin, Sohaib, Sarmad, Khan, Ahsan Raza, Shafi, Muhammad, Khan, Atta ur Rehman
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.01.2021; Elsevier Science Ltd
• 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; Deep learning; LSTM; Passive monitoring; DGRU; Random forest; Human activity recognition; 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...