# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 5 Conclusions  
This paper proposes a multi-person action recognition system, which visualizes the distribution of human body point clouds in three-dimensional space from the signals reflected by millimeter-wave radar and removes the noise due to multipath effects and other influences to obtain high-quality human body point clouds by the traditional filtering methods and a filtering method based on inter-frame differences. Then, the sparsity of the millimeter-wave radar point cloud is overcome, and the amount of data is reduced in multi-person scenarios by superimposing multiple frames and rounding off the \(y\)-axis component. Finally, the spatial and temporal features of human body actions are extracted and classified by a CNN + LSTM network, and a classification accuracy of 92.2% is achieved in multi-person scenarios. We implemented the system using commercial millimeter-wave radar and performed robustness, ablation, and recognition time-cost experiments. The recognition time cost experiments show that the average recognition time of the system proposed in this paper is 66.4 ms for various actions. The ablation experiments show that the average contribution of our proposed frame-difference filtering to overall accuracy under different conditions reaches 8.5%, which fully proves the effectiveness of our proposed frame-difference filtering method, as well as the stability and efficiency of the overall system. In the future, our research will explore the hierarchical classification filtering method for point clouds and further extract more detailed human action features through a multimodal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Activity** & **Time/ms** \\ \hline Boxing & 62 \\ Leg lift & 75 \\ Bowing & 67 \\ Squat down & 69 \\ Stand up & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average recognition time for a single test case for each action of the system in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Activity** & **Time/ms** \\ \hline Boxing & 756 \\ Leg lift & 893 \\ Bowing & 844 \\ Squat down & 747 \\ Stand up & 689 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average recognition time for a single test case for each action of the RadHAR system.  
approach, combining multiple features to improve recognition accuracy and efficiency. This paper's research on human action recognition in multi-person scenarios creates a good foundation for future research directions. It promotes the practical application and development of millimeter-wave radar in human action recognition.  
Conceptualization, X.D. and K.F.; methodology, K.F.; software, K.F.; validation, Y.T., Y.G. and Y.W.; formal analysis, K.F. and F.L.; investigation, K.F.; data curation, K.F.; writing--original draft preparation, K.F.; writing--review and editing, X.D. and F.L.; supervision, X.D.; project administration, X.D.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62162056) and the Industrial Support Foundations of Gansu (Grant No. 2021C7 ZC-06).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all the subjects involved in this study.  
The data are contained within the article.  
The authors declare no conflicts of interest.