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Using Wi-Fi channel state information (CSI) for human activity recognition and fall detection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chowdhury, Tahmid Z. |
| Copyright Year | 2018 |
| Abstract | Human Activity Recognition (HAR) serves a diverse range of human-centric applications in health care, 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 degrade 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. |
| File Format | PDF HTM / HTML |
| DOI | 10.14288/1.0365967 |
| Alternate Webpage(s) | https://open.library.ubc.ca/media/download/pdf/24/1.0365967/4 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
