# Improving Time Study Methods Using Deep Learning-Based Action Segmentation Models
## 8 Conclusions  
This study successfully demonstrates the application of deep learning action segmentation models to improve time study methods in manufacturing, aligning with the operational demands of Industry 4.0. By combining different viewpoints and utilizing advanced feature extraction methods, we have advanced the precision of action segmentation models, as evidenced by the enhanced F1@IoU scores. This research contributes to the broader context by addressing the pressing need for automated and precise human activity analysis in complex manufacturing environments. The evaluation of our models reveals a nuanced understanding of the interconnection between model architecture, feature extraction methods, and viewpoints. The fine-tuning method, which adapts pretrained features to a specific dataset, proved to be particularly effective, suggesting that contextualized learning is crucial for temporal segmentation. Our analysis also highlights the limitations inherent in basic Transformer models for action segmentation tasks, as their global attention mechanism does not compensate for the absence of inductive bias, especially when working with limited datasets. A key limitation of our research lies in the dataset's scope, which, while diverse, may not capture the full complexity of industrial activities. Furthermore, the under-representation of background activities points to the need for datasets that mirror the variability and unpredictability of real-world manufacturing scenarios more closely. Moving forward, several avenues for future research emerge. Investigating the scalability of models to larger and more varied datasets, the integration of multi-modal data sources, and the real-time application of these models in manufacturing settings are pivotal. Additionally, exploring the augmentation of basic Transformer models with structural inductive biases could bridge their current performance gap in the context of manufacturing video datasets. This research contributes to the domain of automated time studies by providing a comprehensive analysis of deep learning-based action segmentation models. The insights garnered here will serve as a motivation for further advancements, ensuring that human efficiency continues to be a cornerstone of productivity in the era of smart industry. Conceptualization, M.G. ([PERSON]); Data curation, M.G. ([PERSON]) and M.G. ([PERSON]); Investigation, M.G. ([PERSON]) and M.H. ; Methodology, M.G. ([PERSON]) and M.H. ; Software, M.G. ([PERSON]) and D.K. ; Validation M.G. ([PERSON]) and D.K. ; Writing--original draft, M.G. ([PERSON]), M.G. ([PERSON]) and M.H. ; Writing--review and editing, M.G. ([PERSON]) and M.H. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Feature Type** & **Viewpoint** & **Number of Parameters** & **Accuracy** & **F1@50** \\ \hline CONV & FT & OH & 380,500 & 96.75 & 89.85 \\ biLSTM & FE & HF & 264,650 & 96.33 & 88.30 \\ CONV & FE & OH & 627,860 & 95.66 & 86.60 \\ biLSTM & FE & OH + HF & 1,057,674 & 96.18 & 85.59 \\ biLSTM & FT & OH & 131,818 & 95.53 & 84.48 \\ TFORM * & TN & OH & 8,960,778 & 96.14 & 83.50 \\ TFORM & FT & OH + HF & 5,268,234 & 96.45 & 83.17 \\ TFORM & TN & OH + HF & 17,918,730 & 94.41 & 82.83 \\ TFORM & FT & OH & 17,394,442 & 95.95 & 82.37 \\ TFORM & TN & HF & 8,960,778 & 96.06 & 81.62 \\ TFORM & FT & HF & 4,743,946 & 95.70 & 80.47 \\ TFORM & FE & OH + HF & 2,105,610 & 95.17 & 78.03 \\ TFORM & FE & HF & 4,743,946 & 95.42 & 78.02 \\ biLSTM & FE & OH & 25,194,506 & 90.38 & 76.14 \\ TFORM & FE & OH & 4,743,946 & 94.08 & 74.25 \\ \hline \hline \end{tabular}
* Best models from each architecture type.  
\end{table} 
Table 4: _Cont._  
**Funding:** This research was funded by the European Regional Development Fund, grant number KK 01.2.1.02.0226. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are openly available.