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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.16301 |
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| _version_ | 1866910089263185920 |
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| author | Golipoor, Sahar Brophy, Richard T. Liu, Ying Ghazalian, Reza Sigg, Stephan |
| author_facet | Golipoor, Sahar Brophy, Richard T. Liu, Ying Ghazalian, Reza Sigg, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16301 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gesture Recognition from body-Worn RFID under Missing Data Golipoor, Sahar Brophy, Richard T. Liu, Ying Ghazalian, Reza Sigg, Stephan Signal Processing 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. |
| title | Gesture Recognition from body-Worn RFID under Missing Data |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.16301 |