## 7 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Data** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **FLOPs** \\ **(M)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Inference** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **F1 Score** \\ **(\%)** \\ \end{tabular} } \\ \hline MobileNetV3-Small-0.75 & RT & 0.86 & 48.0 & 4.85 & 83.49 & 85.87 & 85.49 & 85.61 \\ ShuffleNetV2-0.5 & RT & 0.60 & 82.5 & 2.67 & 94.14 & 94.36 & 94.14 & 94.07 \\ EfficientNetLite0 & RT & 3.38 & 476 & 3.63 & 92.59 & 93.18 & 92.59 & 92.51 \\ MobileViTV3-0.5 & RT & 1.41 & 481 & 4.52 & 81.33 & 84.43 & 81.33 & 81.10 \\ ResNet18 & RT & 11.2 & 2152 & 1.79 & 95.68 & 95.78 & 95.68 & 95.68 \\ DenseNet121 & RT & 6.69 & 3273 & 11.5 & 95.99 & 96.31 & 95.99 & 95.93 \\ CRNN [30] & RT & 0.39 & 63.6 & 14.4 & 88.43 & 88.77 & 88.43 & 88.51 \\ TG2 Net & RT & 0.12 & 52.4 & 3.44 & 94.29 & 94.46 & 94.29 & 97.25 \\ \hline MobileNetV3-Small-0.75 & TD & 0.86 & 48.0 & 4.85 & 95.06 & 95.42 & 95.06 & 95.09 \\ ShuffleNetV2-0.5 & TD & 0.60 & 82.5 & 2.75 & 97.07 & 97.12 & 97.07 & 97.08 \\ EfficientNetLite0 & TD & 3.38 & 476 & 3.83 & 97.07 & 97.10 & 97.07 & 97.05 \\ MobileViTV3-0.5 & TD & 1.41 & 481 & 4.47 & 90.90 & 91.05 & 90.90 & 90.75 \\ ResNet18 & TD & 11.2 & 2152 & 1.77 & 98.46 & 98.47 & 98.46 & 98.45 \\ DenseNet121 & TD & 6.69 & 3273 & 11.7 & 98.46 & 98.52 & 98.46 & 98.45 \\ CRNN & TD & 0.39 & 63.6 & 14.7 & 96.76 & 96.93 & 96.76 & 96.77 \\ TG2 Net & TD & 0.12 & 52.4 & 3.35 & 97.99 & 98.01 & 97.99 & 97.99 \\ \hline TG2-CAFNet & RT+TD & 0.24 & 91.3 & 7.11 & 99.07 & 99.09 & 99.07 & 99.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of model performance. * Introducing continuous action recognition: incorporating models capable of handling continuous actions based on time-series features, such as RNNs, transformers, etc., to better adapt to the dynamic nature of real-world behaviors. * Addressing multi-person scenarios: Future work could explore advanced signal separation techniques or multi-target tracking to reduce interference when multiple individuals are performing actions simultaneously. * Multi-modal integration: Combining radar data with inputs from other sensors (such as visual or inertial data) could help distinguish similar actions and reduce the impact of interference from multiple individuals. Conceptualization, L.H. and D.L. ; methodology, L.H. and D.L. ; software, D.L. ; validation, B.Z., G.C. and H.A. ; formal analysis, M.L. ; investigation, D.L. ; resources, L.H. ; data curation, D.L. and B.Z. ; writing--original draft preparation, D.L. ; writing--review and editing, L.H. ; visualization, D.L. ; supervision, L.H. ; project administration, L.H. ; funding acquisition, L.H. 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 Special Project, grant number 62341311, and the Lanzhou City Talent Innovation and Entrepreneurship Project, grant number 2022-RC-7. Not applicable. The data presented in this study are part of a proprietary dataset. Data are available from the corresponding author upon reasonable request and with permission of the original data holders. Interested researchers may contact the corresponding author via email. The authors declare no conflicts of interest.