M<sup>2</sup>-Fi: Multi-person Respiration Monitoring via Handheld WiFi Devices

Hu, Jingyang; Jiang, Hongbo; Zheng, Tianyue; Hu, Jingzhi; Wang, Hongbo; Cao, Hangcheng; Chen, Zhe; Luo, Jun

doi:10.1109/infocom52122.2024.10621199

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2024

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Cited by 2 publications

References 33 publications

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WiShield: Privacy Against Wi-Fi Human Tracking

Hu,

Jiang,

Chen

et al. 2024

IEEE J. Select. Areas Commun.

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WiShield: Privacy Against Wi-Fi Human Tracking

Hu,

Jiang,

Chen

et al. 2024

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition

Weng,

Wu,

Zheng

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; • Computing methodologies → Artificial intelligence.

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M²-Fi: Multi-person Respiration Monitoring via Handheld WiFi Devices

Cited by 2 publications

References 33 publications

WiShield: Privacy Against Wi-Fi Human Tracking

WiShield: Privacy Against Wi-Fi Human Tracking

Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition

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M2-Fi: Multi-person Respiration Monitoring via Handheld WiFi Devices

Cited by 2 publications

References 33 publications

WiShield: Privacy Against Wi-Fi Human Tracking

WiShield: Privacy Against Wi-Fi Human Tracking

Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition

Contact Info

Product

Resources

About

M²-Fi: Multi-person Respiration Monitoring via Handheld WiFi Devices