Human activity recognition system based on active learning and Wi-Fi sensing
Human activity recognition system based on deep learning and Wi-Fi sensing has gradually become the mainstream research field and has been developed in recent years.However, related systems heavily rely on training with huge labeled samples to reach a high accuracy, which is labor-intensive and unre...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2022-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2022.00262 |
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| Summary: | Human activity recognition system based on deep learning and Wi-Fi sensing has gradually become the mainstream research field and has been developed in recent years.However, related systems heavily rely on training with huge labeled samples to reach a high accuracy, which is labor-intensive and unrealistic for many real-world scenarios.To solve this problem, a system that combines active learning with Wi-Fi based human activity recognition—ALSensing was proposed, which was able to train a well-perform classifier with limited labeled samples.ALSensing was implemented with commercial Wi-Fi devices and evaluated in six real environments.The experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% of total training samples, 58.97% recognition accuracy using 15% of total training samples, while the existing full-supervised system reaches 62.19% recognition accuracy.It demonstrates that ALSensing has a similar performance with baseline but requires much less labeled samples. |
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| ISSN: | 2096-3750 |