Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data
Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address thi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2364 |
| _version_ | 1797211932743172096 |
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| author | Zhanjun Hao Kaikai Han Zinan Zhang Xiaochao Dang |
| author_facet | Zhanjun Hao Kaikai Han Zinan Zhang Xiaochao Dang |
| author_sort | Zhanjun Hao |
| collection | DOAJ |
| description | Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR’s robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints. |
| first_indexed | 2024-04-24T10:34:21Z |
| format | Article |
| id | doaj.art-32aeeacd185c494492c261579692d2aa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:21Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-32aeeacd185c494492c261579692d2aa2024-04-12T13:26:52ZengMDPI AGSensors1424-82202024-04-01247236410.3390/s24072364Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted DataZhanjun Hao0Kaikai Han1Zinan Zhang2Xiaochao Dang3College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaSignificant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR’s robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.https://www.mdpi.com/1424-8220/24/7/2364WiFi sensingcross-domainfew-shot learninghuman activity recognition |
| spellingShingle | Zhanjun Hao Kaikai Han Zinan Zhang Xiaochao Dang Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data Sensors WiFi sensing cross-domain few-shot learning human activity recognition |
| title | Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data |
| title_full | Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data |
| title_fullStr | Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data |
| title_full_unstemmed | Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data |
| title_short | Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data |
| title_sort | wi char a wifi sensing approach with focus on both scenes and restricted data |
| topic | WiFi sensing cross-domain few-shot learning human activity recognition |
| url | https://www.mdpi.com/1424-8220/24/7/2364 |
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