Computer Vision-assisted Wireless Channel Simulation for Millimeter Wave Human Motion Recognition
This study proposes a computer vision-assisted millimeter wave wireless channel simulation method incorporating the scattering characteristics of human motions. The aim is to rapidly and cost-effectively generate a training dataset for wireless human motion recognition, thereby avoiding the laboriou...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2025-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24101 |
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| Summary: | This study proposes a computer vision-assisted millimeter wave wireless channel simulation method incorporating the scattering characteristics of human motions. The aim is to rapidly and cost-effectively generate a training dataset for wireless human motion recognition, thereby avoiding the laborious and cost-intensive efforts associated with physical measurements. Specifically, the simulation process includes the following steps. First, the human body is modeled as 35 interconnected ellipsoids using a primitive-based model, and motion data of these ellipsoids are extracted from videos of human motion. A simplified ray tracing method is then used to obtain the channel response for each snapshot of the primitive model during the motion process. Finally, Doppler analysis is performed on the channel responses of the snapshots to obtain the Doppler spectrograms. The Doppler spectrograms obtained from the simulation can be used to train deep neural network for real wireless human motion recognition. This study examines the channel simulation and action recognition results for four common human actions (“walking” “running” “falling” and “sitting down”) in the 60 GHz band. Experimental results indicate that the deep neural network trained with the simulated dataset achieves an average recognition accuracy of 73.0% in real-world wireless motion recognition. Furthermore, he recognition accuracy can be increased to 93.75% via unlabeled transfer learning and fine-tuning with a small amount of actual data. |
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| ISSN: | 2095-283X |