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FPGA Accelerator for Human Activity Recognition Based on Radar
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AI
DOI：10.1109/TCSII.2023.3328422.png)
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摘要
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Radar-based human activity recognition (HAR) has great potential in many applications. The deployment of HAR in many practical applications requires the system to be energy-resource efficient. In this brief, we propose a lightweight dedicated hardware accelerator for radar-based HAR. In particular, the specific property of radar signals are taken advantages of to optimize the 1-D convolutional neural network (CNN) model for HAR. Based on that, a dedicated hardware architecture is further proposed to exploit the advantages of the optimized CNN model. We build a prototype system on field programmable gate array (FPGA) to validate and demonstrate the proposed design. We also map the proposed accelerator to 55 nm CMOS technology. Experimental results show that the performance as well as the energy-resource efficiency of the proposed design are significantly higher than other existing vision-based HAR systems.
Keyword:
Human activity recognition (HAR)
radar
convolutional neural network (CNN)
lightweight
期刊
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
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引用论文
Acceleration of FPGA Based Convolutional Neural Network for Human Activity Classification Using Millimeter-Wave Radar.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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Classification of Human Activity Based on Radar Signal Using 1-D Convolutional Neural Network基于雷达信号的一维卷积神经网络人体活动分类
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