A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data
Over the past years, device-free sensing has received considerable attention due to its unobtrusiveness. In this regard, context recognition using WiFi Channel State Information (CSI) data has gained popularity, and various techniques have been proposed that combine unobtrusive sensing and deep lear...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/23/7852 |
| _version_ | 1797507194132889600 |
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| author | Augustinas Zinys Bram van Berlo Nirvana Meratnia |
| author_facet | Augustinas Zinys Bram van Berlo Nirvana Meratnia |
| author_sort | Augustinas Zinys |
| collection | DOAJ |
| description | Over the past years, device-free sensing has received considerable attention due to its unobtrusiveness. In this regard, context recognition using WiFi Channel State Information (CSI) data has gained popularity, and various techniques have been proposed that combine unobtrusive sensing and deep learning to accurately detect various contexts ranging from human activities to gestures. However, research has shown that the performance of these techniques significantly degrades due to change in various factors including sensing environment, data collection configuration, diversity of target subjects, and target learning task (e.g., activities, gestures, emotions, vital signs). This problem, generally known as the domain change problem, is typically addressed by collecting more data and learning the data distribution that covers multiple factors impacting the performance. However, activity recognition data collection is a very labor-intensive and time consuming task, and there are too many known and unknown factors impacting WiFi CSI signals. In this paper, we propose a domain-independent generative adversarial network for WiFi CSI based activity recognition in combination with a simplified data pre-processing module. Our evaluation results show superiority of our proposed approach compared to the state of the art in terms of increased robustness against domain change, higher accuracy of activity recognition, and reduced model complexity. |
| first_indexed | 2024-03-10T04:45:09Z |
| format | Article |
| id | doaj.art-1fb29fdaf50349ddbff010b5d6d57a41 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:45:09Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1fb29fdaf50349ddbff010b5d6d57a412023-11-23T03:00:03ZengMDPI AGSensors1424-82202021-11-012123785210.3390/s21237852A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI DataAugustinas Zinys0Bram van Berlo1Nirvana Meratnia2Interconnected Resource-Aware Intelligent Systems Cluster, Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The NetherlandsInterconnected Resource-Aware Intelligent Systems Cluster, Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The NetherlandsInterconnected Resource-Aware Intelligent Systems Cluster, Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The NetherlandsOver the past years, device-free sensing has received considerable attention due to its unobtrusiveness. In this regard, context recognition using WiFi Channel State Information (CSI) data has gained popularity, and various techniques have been proposed that combine unobtrusive sensing and deep learning to accurately detect various contexts ranging from human activities to gestures. However, research has shown that the performance of these techniques significantly degrades due to change in various factors including sensing environment, data collection configuration, diversity of target subjects, and target learning task (e.g., activities, gestures, emotions, vital signs). This problem, generally known as the domain change problem, is typically addressed by collecting more data and learning the data distribution that covers multiple factors impacting the performance. However, activity recognition data collection is a very labor-intensive and time consuming task, and there are too many known and unknown factors impacting WiFi CSI signals. In this paper, we propose a domain-independent generative adversarial network for WiFi CSI based activity recognition in combination with a simplified data pre-processing module. Our evaluation results show superiority of our proposed approach compared to the state of the art in terms of increased robustness against domain change, higher accuracy of activity recognition, and reduced model complexity.https://www.mdpi.com/1424-8220/21/23/7852device-free sensingunobtrusive sensingWiFi CSIgenerative adversarial networkdomain changedomain adaptation |
| spellingShingle | Augustinas Zinys Bram van Berlo Nirvana Meratnia A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data Sensors device-free sensing unobtrusive sensing WiFi CSI generative adversarial network domain change domain adaptation |
| title | A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data |
| title_full | A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data |
| title_fullStr | A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data |
| title_full_unstemmed | A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data |
| title_short | A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data |
| title_sort | domain independent generative adversarial network for activity recognition using wifi csi data |
| topic | device-free sensing unobtrusive sensing WiFi CSI generative adversarial network domain change domain adaptation |
| url | https://www.mdpi.com/1424-8220/21/23/7852 |
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