WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910687122423808 |
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| author | Strohmayer, Julian Wödlinger, Matthias Kampel, Martin |
| author_facet | Strohmayer, Julian Wödlinger, Matthias Kampel, Martin |
| contents | We propose WiFlexFormer, a highly efficient Transformer-based architecture designed for WiFi Channel State Information (CSI)-based person-centric sensing. We benchmark WiFlexFormer against state-of-the-art vision and specialized architectures for processing radio frequency data and demonstrate that it achieves comparable Human Activity Recognition (HAR) performance while offering a significantly lower parameter count and faster inference times. With an inference time of just 10 ms on an Nvidia Jetson Orin Nano, WiFlexFormer is optimized for real-time inference. Additionally, its low parameter count contributes to improved cross-domain generalization, where it often outperforms larger models. Our comprehensive evaluation shows that WiFlexFormer is a potential solution for efficient, scalable WiFi-based sensing applications. The PyTorch implementation of WiFlexFormer is publicly available at: https://github.com/StrohmayerJ/WiFlexFormer. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_04224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing Strohmayer, Julian Wödlinger, Matthias Kampel, Martin Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning We propose WiFlexFormer, a highly efficient Transformer-based architecture designed for WiFi Channel State Information (CSI)-based person-centric sensing. We benchmark WiFlexFormer against state-of-the-art vision and specialized architectures for processing radio frequency data and demonstrate that it achieves comparable Human Activity Recognition (HAR) performance while offering a significantly lower parameter count and faster inference times. With an inference time of just 10 ms on an Nvidia Jetson Orin Nano, WiFlexFormer is optimized for real-time inference. Additionally, its low parameter count contributes to improved cross-domain generalization, where it often outperforms larger models. Our comprehensive evaluation shows that WiFlexFormer is a potential solution for efficient, scalable WiFi-based sensing applications. The PyTorch implementation of WiFlexFormer is publicly available at: https://github.com/StrohmayerJ/WiFlexFormer. |
| title | WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2411.04224 |