# ML-Based Pain Recognition Model Using Mixup Data Augmentation
## 5 Conclusions and Future Work  
This section includes a summary of the research conducted and its future scope. This study explored the application of mixup, a data-agnostic and straightforward data augmentation principle for pain intensity classification based on physiological data focusing on EDA. We explored machine-learning architectures that could effectively be used for this application with and without mixup. We used the BioVid Heat Pain Database (PART A) and designed a binary classifier that was able to distinguish between BL1 (\"no pain\") and PA4 (\"pain\"). An SVM was used for this purpose that was trained with and without augmented data. The data augmentation that was used is called mixup. Incorporating mixup into the training pipeline required only a few lines of code and introduced minimal computational overhead. A comparison between architectures employing mixup and those that were trained without augmented data was made, and it was observed that the performance of the models using mixup was better than that of the ones without mixup. All designed architectures were trained comprehensively, within the constraints of the computational resources available for model development. Therefore, refining different components of the models through additional adjustments is believed to enhance the system's performance potentially. The efficacy of mixup augmentation relies heavily on the parameter \(\alpha\), which determines the extent of augmentation, and thus requires a careful investigation to achieve better performance. Despite the promising results, certain limitations could affect the model's performance in practical applications. Some noteworthy limitations are described below:  
* Dataset: The data used for training the machine learning model were sourced from a controlled laboratory/clinical setting. Various factors such as sensor quality, individual differences, environmental factors, and variations in real-world EDA signals might impact the model's overall performance. * Sensitivity to Mixup Parameter: The performance of mixup relies on the proper selection of the mixup parameter \(\alpha\), with inappropriate values potentially leading to the unrealistic performance of the model and thus to inaccuracies. * Potential Overfitting to Augmented Data: Although mixup reduces overfitting, there might be a possibility of overfitting to the specific patterns generated by the augmentation data. The robustness can be improved by combining mixup with other regularization techniques. * Clinical Validation: Clinical adaptation requires validation across diverse patient cohorts. Several clinical trials might be required to build trust among healthcare professionals and confirm the model's reliability. * Choice of Model: In this study, a classical machine learning model was used for pain recognition that was built upon a set of carefully extracted features from raw data, and then mixup was applied to these manually extracted features before training the model. However, because we depended on manually extracted features, the overall accuracy and performance would not be as good as a pain recognition model built using deep learning architectures, where the features are extracted automatically without any user intervention. * Computational Constraints: Real-time applications on resource-limited devices may require further model optimization to ensure speed and efficiency without sacrificing accuracy. Exploring hardware-optimized versions and model compression techniques could facilitate deployment in wearable devices. * Use of Classical Mixup Technique Only: In our research, we applied classical linear interpolation for mixup, our main focus being the application of mixup on a time-series physiological signal (EDA). However, there are also numerous alternative mixup techniques as discussed in the Literature Review section, such as manifold mixup and AdaMixup, that have been developed in other applications and shown to improve model performance and robustness.