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Title	Device Free Human Activity Recognition Using Wifi Channel State Information
ID_Doc	19832
Authors	Damodaran N.; Schäfer J.
Year	2019
Published	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI	http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00205
Abstract	Human Activity Recognition (HAR) is a rather broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in-order to mitigate or avoid these limitations, device free solutions based on radio signals like home WiFi are considered. Recently, channel state information (CSI), available in WiFi networks have been proposed for fine-grained analysis. We are able to detect the human activities like Walk, Stand, Sit, Run, etc. in a Line of Sight scenario (LOS) and a Non Line of Sight (N-LOS) scenario within an indoor environment. We propose two algorithms - one using a support vector machine (SVM) for classification and another one using a long short-term memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques the latter processes the raw data directly (after denoising with wavelets). We show that it is possible to characterize activities and / or human body presence with high accuracy and we compare both approaches with regards to accuracy and performance. © 2019 IEEE.
Author Keywords	Activity recognition; Ambient assisted living; Channel state information (CSI); Fingerprinting; Human activity recognition; Localization; Machine learning; Neural networks; Object detection; Passive (microwave) remote sensing; Passive radar

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