# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 5 Conclusions  
In this study, we proposed a low-power and lightweight HAR system using radar point-cloud data. The proposed HAR system consists of pre-processing 3D point data with four channels and then inputting the converted 2D image through a PFE to the image classification network. For the HAR technology to be practical, a lightweight and low-power design must be used. In this paper, PFE is used as an encoder for point-cloud data, and an image classification network based on depth-wise separable convolution is used to realize a lightweight HAR system. Also, the 4 bit quantized classification network was implemented on an FPGA to achieve a low-power design. The 32 bit format classification network was quantized to a 4 bit one, resulting in a lightweight design that used 12.5% of the  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Proposed System** \\ \hline Platform & ZCU104 \\ Execution time & 2.43 ms \\ CLB LUTs & 29,720 \\ CLB Registers & 22,893 \\ DSPs & 72 \\ Block RAMs & 25.5 \\ Frequency & 300 MHz \\ Power & 3.479 W \\ \hline \end{tabular}
\end{table}
Table 7: Specifications of the HAR system implementation on the Zynq UltraScale+ ZCU104.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Feature Size** & **\#Data Classes** & **Accuracy** & **\#Parameters** & **Platform** & **Power** \\ \hline
[11] & \(10\times 32\times 32\) & 5 & 90.47\% & 291 K & - & - \\
[12] & \(3\times 224\times 224\) & 7 & 96.10\% & 1510 K & - & - \\
[13] & \(10\times 32\times 32\) & 6 & 90.20\% & 131.62 K & GTX3080 & 320 W \\
[14] & \(9\times 400\times 3\) & 6 & 94.53\% & 1610 K & RTX2060 & 175 W \\
[15] & \(2\times 25\times 3\) & 5 & 95.12\% & 79.7 K & Raspberry Pi & 3.6 W \\ \hline Ours & \(64\times 64\times 64\) & 11 & 94.79\% & 22.28 K & ZCU104 & 3.479 W \\ \hline \end{tabular}
\end{table}
Table 8: Performance of the reference HAR system versus the proposed HAR system.  
memory of the original 32 bit format network. As a result of the implementation, in terms of network complexity, 25.77 M MAC operations were performed and 22.28 K parameters were used, which is the smallest among the compared HAR systems. An accuracy of 95.54% was achieved with a 32 bit floating-point data format in a GPU environment and 94.79% with a 4 bit fixed-point data format in a hardware-software implementation environment for 11 classes of datasets collected using the FMCW MIMO millimeter-wave radar. In terms of power consumption, the proposed HAR system consumed 3.479 W, which is the lowest power consumption compared to other HAR systems. The results show that the proposed HAR system has a level of accuracy similar to other HAR systems, has relatively low complexity, and can operate at low power. In future work, we plan to implement a low-power, lightweight HAR system on a very-large-scale integrated circuit (VLSI) to achieve a better low-power, lightweight design while maintaining current accuracy.  
[PERSON]. ([PERSON]) designed and implemented the proposed HAR system, performed the experiments and evaluation, and wrote the paper. [PERSON] evaluated the proposed HAR system and revised the manuscript. [PERSON]. ([PERSON]) evaluated the proposed HAR system and revised the manuscript. [PERSON] conceived of and led the research, analyzed the experimental results, and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (Nos. 00144290 and 00433615) and funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea); the CAD tools were supported by IDEC.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.