Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Open accessAbstract
Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.
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“Additionally, HAR systems incorporate vision-based [32], wireless-based [16, 21, 50, 61, 66, 67], and mobile-based [17, 24, 27, 38, 40, 41, 43, 55, 57, 59] methods.”
- Authors
- Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., & Yang, Z.
- Affiliations
- Tsinghua University (CN)
- Michigan State University (US)
- University of Maryland, College Park (US)
- Venue
- Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
- Publisher
- ACM
- Published
- 2019-06-12
- Pages
- 313-325
- OpenAlex
- W2950821050
- Cited by
- 557
- References
- 56
- Citations by year
- 2026: 57 · 2025: 96 · 2024: 75 · 2023: 94 · 2022: 96 · 2021: 88 · 2020: 45 · 2019: 6
- FWCI
- 47.58
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Mobile Systems, Applications, and Services (publishedVersion)
- Funders
- National Key Research Plan (No. 2016YFC0700100)
- National Science Foundation (CNS-1837146)
- National Natural Science Foundation of China (61832010, 61632008, 61672319, 61872081)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- WiFi sensing, gesture recognition, cross-domain recognition, domain-independent features, velocity profiles, ubiquitous sensing
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