Summary of Directional Antenna Systems For Long-range Through-wall Human Activity Recognition, by Julian Strohmayer and Martin Kampel
Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition
by Julian Strohmayer, Martin Kampel
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes two novel WiFi Channel State Information (CSI)-based human activity recognition (HAR) systems using variants of the Espressif ESP32 microcontroller. The first system combines an ESP32-S3 with a directional biquad antenna, while the second uses the built-in printed inverted-F antenna (PIFA) and a plane reflector for directionality. Both systems are evaluated in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios using the Wallhack1.8k dataset and an EfficientNetV2-based activity recognition model. The results show that both systems can achieve high accuracy in recognizing human activities through walls, with the biquad antenna system achieving 92% accuracy and the PIFA-based system achieving 87% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to recognize human activities using WiFi signals. This is useful because it allows us to track people’s movements without needing cameras or other sensors. The researchers used special microcontrollers called ESP32-S3s, which can be paired with different antennas to improve the signal quality. They tested two different systems and found that they could accurately identify people’s activities even when there were walls in the way. This technology has many potential applications, such as tracking people’s movements in large buildings or monitoring people’s health. |
Keywords
* Artificial intelligence * Activity recognition * Tracking
