# ML-Based Pain Recognition Model Using Mixup Data Augmentation
## 2 Literature Review  
As stated in the previous section, it is difficult to interpret pain, especially when the patient cannot communicate his or her pain experience. Detecting pain has always been tricky because there is no easy method to measure and detect the presence of pain directly. Most of the methods available in the past rely solely on techniques such as verbal communication and tend to lack reliability. However, advancements in artificial intelligence have eased the process of pain recognition by offering an automated detection of medical symptoms. Artificial intelligence requires a large amount of data with which it learns to be able to then make predictions on unseen data. The authors of [6] present a multimodal dataset, in which pain is induced at different levels. This dataset is called Biopotential and Video (BioVid) Heat Pain Database and is used for research purposes. This section presents some of the well-established machine learning approaches, some of which make use of the BioVid dataset for pain recognition; additionally, mixup, a data augmentation technique, and its prior applications in domains like speech and images are covered. There is constantly growing work focusing on pain recognition using physiological signals, as also stated in [10]. Categorization is then based on the nature of pain elicitations. Many authors have proposed traditional machine learning approaches such as decision trees or support vector machines (SVMs) that involve using a set of carefully engineered features extracted from raw data. The extracted features are then used to optimize an inference model. Some of them are worth noting. The authors in [15] proposed a pain detection approach based on Electroencephalogram (EEG) signals. Choi-Williams quadratic time-frequency distribution was used to extract necessary features from EEG signals, and then the extracted features were used to train an SVM, which was then able to recognize pain based on the classification task. In [16], camera photoplethysmography (PPG) signals were combined with ECG and EMG signals, and the dataset that was then obtained was used for pain intensity classification based on a fusion architecture at the feature level with SVMs and random forests as base classifiers. The authors in [9; 17] used the BVDB dataset to perform different pain intensity classification experiments based on a carefully selected set of features extracted from both physiological and video signals, and a classification task was then performed based on random forest and SVM classifiers. In [18], a user-independent pain intensity classification evaluation based on physiological signals using the same dataset was carried out. An adaptive confidence learning approach for personalized pain estimation was also proposed by the same authors based on both physiological and video modalities, and the fusion was applied at the feature level. Here, random forests were used to solve the regression task. The authors of [19] proposed a classification approach based on Deep Belief Networks (DBNs). The work mainly focused on the assessment of patients' pain levels during surgery using photoplethysmography (PPG) signals. However, it is to be noted that in [19], the proposed architecture was built on a set of manually engineered features extracted from PPG signals. Now that we have seen the benefits of machine learning techniques, especially in pain detection, the efficiency of these techniques might be limited because they exhibit undesirable behaviors on adversarial examples and unseen data that impact their overall performance. Several solutions have been proposed to overcome these undesirable behaviors. Mixup, defined by the authors in [11] as a data augmentation technique, is one such solution to overcome these limitations. Mixup trains a neural network on convex combinations of pairs of examples and their labels.