Gesture Recognition from body-Worn RFID under Missing Data
arXiv:2601.16301v2 Announce Type: replace Abstract: We explore hand-gesture recognition through the use of passive body-worn reflective tags. A data processing pipeline is proposed to address the issue of missing data. Specifically, missing information is recovered through linear and exponential interpolation and extrapolation. Furthermore, imputation and proximity-based inference are employed. We represent tags as nodes in a temporal graph, with edges formed based on correlations between received signal strength (RSS) and phase values across successive timestamps, and we train a graph-based convolutional neural network that exploits graph-based self-attention. The system outperforms state-of-the-art methods with an accuracy of 98.13% for the recognition of 21 gestures. We achieve 89.28% a
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Abstract:We explore hand-gesture recognition through the use of passive body-worn reflective tags. A data processing pipeline is proposed to address the issue of missing data. Specifically, missing information is recovered through linear and exponential interpolation and extrapolation. Furthermore, imputation and proximity-based inference are employed. We represent tags as nodes in a temporal graph, with edges formed based on correlations between received signal strength (RSS) and phase values across successive timestamps, and we train a graph-based convolutional neural network that exploits graph-based self-attention. The system outperforms state-of-the-art methods with an accuracy of 98.13% for the recognition of 21 gestures. We achieve 89.28% accuracy under leave-one-person-out cross-validation. We further investigate the contribution of various body locations on the recognition accuracy. Removing tags from the arms reduces accuracy by more than 10%, while removing the wrist tag only reduces accuracy by around 2%. Therefore, tag placements on the arms are more expressive for gesture recognition than on the wrist.
Subjects:
Signal Processing (eess.SP)
Cite as: arXiv:2601.16301 [eess.SP]
(or arXiv:2601.16301v2 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2601.16301
arXiv-issued DOI via DataCite
Submission history
From: Sahar Golipoor [view email] [v1] Thu, 22 Jan 2026 20:07:42 UTC (1,969 KB) [v2] Tue, 31 Mar 2026 15:09:13 UTC (1,977 KB)
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