# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 5 Discussion  
In this study, the proposed emotion estimation method based on environmental data demonstrated its potential, especially for valence estimation. However, the performance of the models varied, and several interesting findings emerged from the analysis. Figure 34: RMSE of arousal (validation data) during the GBDT model’s training for subject 6. Figure 32: R\({}^{2}\) score of arousal (validation data) during the GBDT model’s training for subject 6. Figure 33: RMSE of arousal (training data) during the GBDT model’s training for subject 6. The RF model and the GBDT model exhibited high accuracy, with the RF model consistently outperforming the GBDT model across all subjects. One possible explanation for this result is that the RF model is particularly effective at handling noise and non-linear relationships in the data, as it constructs multiple decision trees and combines their outputs. This ability to handle noisy environmental data makes the RF model a robust choice for emotion estimation. On the other hand, the GBDT modelproduced results that were inferior to those of the RF model in this experiment. This could be attributed to the fact that the study focused on exploring the potential of various estimation methods, rather than optimizing the models. While the GBDT model has been shown to perform well in various contexts, its performance in this study could have been hindered by factors such as suboptimal hyperparameter settings or the complexity of the data. Future work should focus on tuning the parameters of the GBDT model to explore its full potential. However, it is also important to consider the practical implications of hyperparameter tuning in real-world applications. In operational settings, the ability to achieve high performance with minimal parameter adjustment can be a significant advantage, particularly in time-sensitive or resource-constrained environments. In this regard, the RF model's inherent robustness and its lesser dependency on hyperparameter tuning make it an appealing choice. Unlike the GBDT model, which often requires careful adjustment of parameters such as learning rate and tree depth to avoid issues like overfitting or underfitting, the RF model can deliver stable and reliable results with default settings. This characteristic reduces the need for extensive trial-and-error during model setup and makes the RF model a more practical solution for emotion estimation in scenarios where ease of deployment and consistent performance are priorities. In this experiment, the performance of the CNN-LSTM model showed variability in the loss and accuracy curves on the validation data. This variability may stem from insufficient generalization caused by underfitting. Furthermore, significant fluctuations were observed in the accuracy and loss values across epochs, suggesting instability in the learning process due to an excessively high learning rate. To mitigate these issues, several improvements can be considered. First, adjusting the learning rate could stabilize the training process, leading to more consistent learning outcomes. Second, implementing data augmentation techniques and optimizing early stopping conditions may help reduce performance discrepancies across folds and achieve more stable results. By increasing the amount of training data and refining the training process, the model's generalization ability could be enhanced. Additionally, introducing more complex network architectures may enable the model to better capture patterns in the training data and learn a broader range of features. This could lead to improved accuracy and potentially allow the CNN-LSTM model to outperform other models. The incorporation of advanced methods such as regularization, dropout, or batch normalization may further enhance the model's robustness and reduce overfitting. Addressing these challenges is expected to significantly improve the performance of the CNN-LSTM model, both in terms of accuracy and stability, making it a more competitive approach for emotion estimation tasks.