Multi-User Macro Gesture Recognition using mmWave Technology

Ninos, Alexandros; Hasch, Jürgen; Zwick, Thomas

doi:10.23919/eurad50154.2022.9784494

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Due to the non-directly observable nature of wireless signals, unlike directly visible videos or images, they can protect user privacy. Extensive recent efforts with various wireless signals, such as Wi-Fi [ 16 , 17 , 18 ], RFID [ 19 , 20 , 21 ], acoustic [ 22 , 23 , 24 ] and mmWave [ 25 , 26 , 27 ], have been devoted to inferring gestures. Wi-Fi devices can be easily and inexpensively deployed in most home and office environments, which makes them more promising.…”

Section: Introductionmentioning

confidence: 99%

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi

Xie,

Li,

Feng

et al. 2024

Sensors

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RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

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