Environment and Person-independent Gesture Recognition with Non-static RFID Tags Leveraging Adaptive Signal Segmentation
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A4 Artikkeli konferenssijulkaisussa
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2024
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en
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8
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IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Abstract
Gesture recognition for human machine interaction enhances the efficiency, safety, and usability of industrial and factory automation systems. We investigate hand-gesture recognition using battery-less body-worn reflective tags. Particularly, we propose two methods for hand gesture recognition using radio frequency identification (RFID). From backscattered signals we utilize in-phase and quadrature (IQ) constellation, as well as the phase. We convert extracted IQ samples into images and interprete them for gestures using a pre-trained VGG16. As a second approach we alternatively conduct pre-processing on the phase of the backscattered signals and propose Zero Crossing-Modified Derivative (ZCMD) for signal segmentation. Through signal resampling and wavelet denoising we mitigate undesired fluctuations introduced during this process, while retaining crucial signal characteristics. Subsequently, we integrate time-domain and frequency-domain features of the signals and train a random forest classifier based on these features to identify different gestures. Utilizing battery-free body-worn RFID tags, we are able to outperform a state-of-the art method and recognize four gestures with an accuracy of 81 % with the VGG16-based model. Employing phase, we achieve an accuracy of 94 %.Description
Publisher Copyright: © 2024 IEEE. | openaire: EC/HE/101071179/EU//SUSTAIN
Keywords
gesture recognition, human-sensing, RFID, sig-nal processing, signal segmentation
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Citation
Golipoor, S & Sigg, S 2024, Environment and Person-independent Gesture Recognition with Non-static RFID Tags Leveraging Adaptive Signal Segmentation . in T Facchinetti, A Cenedese, L L Bello, S Vitturi, T Sauter & F Tramarin (eds), 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation, ETFA 2024 . IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, IEEE, IEEE International Conference on Emerging Technologies and Factory Automation, Padova, Italy, 10/09/2024 . https://doi.org/10.1109/ETFA61755.2024.10710733