MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference
Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1978 |
| _version_ | 1797239281035509760 |
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| author | Weiling Zheng Yu Zhang Landu Jiang Dian Zhang Tao Gu |
| author_facet | Weiling Zheng Yu Zhang Landu Jiang Dian Zhang Tao Gu |
| author_sort | Weiling Zheng |
| collection | DOAJ |
| description | Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.17</mn><mo>%</mo></mrow></semantics></math></inline-formula> on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost. |
| first_indexed | 2024-04-24T17:49:02Z |
| format | Article |
| id | doaj.art-0af2eb6b5d8c4e61b419706d43a85838 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:49:02Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0af2eb6b5d8c4e61b419706d43a858382024-03-27T14:04:17ZengMDPI AGSensors1424-82202024-03-01246197810.3390/s24061978MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal InterferenceWeiling Zheng0Yu Zhang1Landu Jiang2Dian Zhang3Tao Gu4School of Computing Technologies, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, AustraliaSchool of Computing, Macquarie University, 4 Research Park Drive, North Ryde, NSW 2109, AustraliaBase of Red Bird MPhil, HKUST(GZ) University, No.1 Du Xue Rd., Guangzhou 511458, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Blvd, Shenzhen 518060, ChinaSchool of Computing, Macquarie University, 4 Research Park Drive, North Ryde, NSW 2109, AustraliaRadio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.17</mn><mo>%</mo></mrow></semantics></math></inline-formula> on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.https://www.mdpi.com/1424-8220/24/6/1978device-free behavioral sensingorthogonal signal interferenceuser identification |
| spellingShingle | Weiling Zheng Yu Zhang Landu Jiang Dian Zhang Tao Gu MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference Sensors device-free behavioral sensing orthogonal signal interference user identification |
| title | MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference |
| title_full | MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference |
| title_fullStr | MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference |
| title_full_unstemmed | MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference |
| title_short | MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference |
| title_sort | meshid few shot finger gesture based user identification using orthogonal signal interference |
| topic | device-free behavioral sensing orthogonal signal interference user identification |
| url | https://www.mdpi.com/1424-8220/24/6/1978 |
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