# ML-Based Pain Recognition Model Using Mixup Data Augmentation
## 1 Introduction  
Artificial intelligence (AI) dates back to 1950 as described in [1], where computers were used to simulate intelligent behavior. [PERSON] described this act of simulating intelligence as AI. Began as a simple software program, AI is now advanced, includes several complex algorithms, and can even perform functions similar to a human brain. AI has evolved significantly over the past few years, especially with the advent of machine learning (ML) and deep learning (DL) [2]. ML is an evolving branch of computational algorithms designed to emulate human intelligence. ML algorithms can learn from past data and improve their predictive accuracy over time. ML is widely used in the healthcare industry, making its applications seamless. Such applications include diagnosing diseases and predicting the therapeutic response, as outlined in [1]. Several studies, including [3], have also stated that it is possible to detect the early stages of cancer, which was almost impossible in the past. Although AI was already studied in the mid-1990s, its applications in medicine were limited because of the problem of \"overfitting\", and this limitation was overcome in the 2000s with the availability of larger datasets and several regularization methods. Because of ML's ability to analyze large datasets and identify minor patterns that are not possible through conventional techniques, it can be used to identify pain in humans. Pain is a complex phenomenon and is difficult to interpret without any communication from the patient because pain is an individual sensation. No easy methods are available to measure pain directly, making it challenging to detect and identifying[4, 5]. Health professionals must rely on the patient's description of the pain's location, quality, and intensity. This makes the management of pain difficult and this mostly affects patients with limited communication abilities who cannot report their pain experience. Such vulnerablegroups are infants and elderly people, which makes it difficult for medical professionals to come to a concrete conclusion or solution. Techniques such as the visual analog scale (VAS) or the numeric rating scale (NRS) make it possible to quantify pain, as also mentioned in [6]. However, these methods only work when the patient is sufficiently alert and cooperative, which is not always possible in practical circumstances such as post-surgery phases. The lack of efficient pain measurement techniques may lead to cardiac stress in at-risk patients, the under-perfusion of the operating field, or the chronification of pain. These challenges highlight the need and importance of an automatic pain recognition model. Many studies in the literature, including [7, 8], have focused on exploring the relationship between pain and the central nervous system, utilizing fMRI and EEG, as also stated in [6], in the field of automatic pain recognition. However, research aiming at a practical application of findings in the field of automatic pain recognition was non-existent. This limitation motivated the authors of [6] to develop a multimodal dataset that was based on biopotentials and video. This study aimed to select the features and feature patterns that contribute to the highest recognition rate for pain recognition from emotion. Additionally, ML techniques driven by data hold significant potential in automating pain recognition, thereby assisting healthcare professionals in delivering appropriate medical interventions and treatments. Hence, a combination of this new dataset, introduced in [6], and AI paved the way for more efficient automatic pain recognition models. Such automatic pain recognition could be carried out in several ways, for example, through facial expressions, speech, body movements, and/or by using biophysical signals such as Electromyography (EMG), Electrocardiogram (ECG), and electrodermal activity (EDA/GSR), as demonstrated in [9].