## 6 Conclusions  
This study presented a multi-modal deep learning framework for recognizing excavator activities using synchronized RGB and point cloud data streams. Unlike previous approaches that rely solely on single-source RGB imagery, this two-stream CNN-LSTM \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Data** & **Algorithm** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Loss** \\ \hline
**RGB** * & CNN-LSTM & 90.67 & 90.67 & 90.89 & 90.78 & 9.33 \\
**PC** ** & CNN-LSTM & 92.00 & 92.00 & 92.28 & 92.14 & 8.00 \\
**Multi-St.*** & Two-stream & 94.67 & 94.67 & 94.90 & 94.78 & 5.33 \\  & CNN-LSTM & & & & & \\ \hline \hline \end{tabular}  
* RGB Camera = RGB, ** Point Cloud = FC, *** Multi-stream = Multi-St.  
\end{table} 
Table 6: Comparison of the model performance. model leverages the complementary strengths of 2D visual textures and 3D geometric depth to deliver robust activity recognition even under occlusion, variable lighting conditions, and complex site environments. The model achieved a training accuracy of 99.62% and a test accuracy of 94.67%, significantly outperforming single-stream RGB (90.67%) and point cloud (92.00%) CNN-LSTM models on the same dataset. These results demonstrate the effectiveness of spatial-temporal feature fusion in handling ambiguities between visually similar activities such as Approach and Leveling. The key academic contributions of this study include the development of a two-stream CNN-LSTM architecture for fusing RGB and point cloud data, the creation of a large-scale synchronized dataset comprising 495,000 labeled samples, the introduction of a standard labeling scheme for multi-modal activity annotation, and a comprehensive performance comparison with single-stream models. The proposed method consistently outperformed existing baselines, confirming the advantages of multi-modal fusion for robust excavator activity recognition in dynamic construction environments. The study has demonstrated that the proposed method significantly improves the recognition of excavator activities over recent state-of-the-art existing single-stream models, resulting in enhanced safety, improved efficiency, and automation of earthwork operations. However, there are some limitations to consider. The model's performance in recognizing activities is optimized for a single excavator and may be affected when multiple excavators are present. Moreover, the study did not account for the impact of surrounding equipment, particularly the movement of dump trucks. Such interactions may lead to occlusions or overlapping motion, which can potentially affect recognition accuracy. Considering these limitations, the future objectives of this study are to (i) enhance the model's architecture to enable recognition of activities for multiple excavators simultaneously; (ii) expand the collection of data to include different excavator activities from various construction sites with diverse earthen materials; (iii) explore methods to enhance the AI model's robustness in multi-equipment environments by incorporating occlusion handling and contextual understanding. In addition to these goals, this study presents opportunities for integrating the proposed recognition system into broader AI-driven health monitoring platforms. By continuously observing operational patterns and behaviors through multi-stream data, the system could evolve to detect abnormal equipment states, anticipate component wear, and support prescriptive maintenance decisions. This paves the way for real-time digital twin environments where performance optimization, fault prediction, and safety management converge into a unified monitoring framework. Conceptualization, H.S.C., K.L., A.S. and J.S. ; Methodology, H.S.C., K.L. and A.S.; Software, H.S.C. and K.L. ; Validation, A.S. and K.L. ; Formal analysis, H.S.C., A.S. and K.L. ; Investigation, H.S.C., A.S. and K.L. ; Resources, H.S.C. and J.S. ; Data curation, A.S. and K.L. ; Writing--original draft, H.S.C. and K.L. ; Writing--review and editing, A.S. and J.S.