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Using Wi-Fi channel state information (CSI) for human activity recognition and fall detection

University of British Columbia

Human Activity Recognition (HAR) serves a diverse range of human-centric applications in healthcare, smart homes, and security. Recently, Wi-Fi-based solutions have attracted a lot of attention. The underlying principle of these is the effect that human bodies have on nearby wireless signals. The presence of static objects such as ceilings and furniture cause reflections while dynamic objects such as humans result in additional propagation paths. These effects can be empirically observed by monitoring the Channel State Information (CSI) between two Wi-Fi devices. As different human postures induce different signal propagation paths, they result in unique CSI signatures, which can be mapped to corresponding human activities. However, there are some limitations in current state-of-the-art solutions. First, the performance of CSI-based HARs degrades in complex environments. To overcome this limitation, we propose Wi-HACS: Leveraging Wi-Fi for Human Activity Classification using Orthogonal Frequency Division Multiplexing (OFDM) Subcarriers. In our work, we propose a novel signal segmentation method to accurately determine the start and end of a human activity. We use several signal pre-processing and noise attenuation techniques, not commonly used in CSI-based HAR, to improve the features obtained from the amplitude and phase signals. We also propose novel features based on subcarrier correlations and autospectra of principal components. Our results indicate that Wi-HACS can outperform the state-of-the-art method in both precision and recall by 8% in simple environments, and by 14.8% in complex environments. The second limitation in existing CSI-HAR solutions is their poor performance in new/untrained environments. Since accurate Wi-Fi based fall detectors can greatly benefit the well-being of the elderly, we propose DeepFalls: Using Wi-Fi Spectrograms and Deep Convolutional Neural Nets for Fall Detection. We utilize the Hilbert Huang Transform spectrograms and train a Convolutional Neural Network to learn the features automatically. Our results show that DeepFalls can outperform the state-of-the-art RT-Fall in untrained environments with improvements in sensitivity and specificity by 11% and 15% respectively.

Text

2018-04-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65593

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/