# ML-Based Pain Recognition Model Using Mixup Data Augmentation
## 3 Methodology  
### The BioVid Heat Pain Database (BVDB)  
Physiological signals play a vital role in pain assessment. We now need a methodology to collect these signals and form a database to be used as input to the AI model. The BioVid Heat Pain Database (BVDB), introduced by the authors of [6], is used to meet these requirements. The BVDB was collected in collaboration with the Neuro-Information Technology group of the University of Magdeburg and the Medical Psychology group of the University of Ulm. It includes multimodal data recordings from healthy subjects wherein the subjects are subjected to different levels of artificial pain stimuli. Pain elicitation in the form of heat was conducted through the professionally designed PATHWAY ([[https://www.medoc-web.com/pathway](https://www.medoc-web.com/pathway)]([https://www.medoc-web.com/pathway](https://www.medoc-web.com/pathway)) (accessed on 1 August 2024)) thermode attached to the participants' right forearm. Three physiological signals, namely, EDA, ECG, and EMG, were recorded during this experiment. Along with the physiological signals, video signals were also collected; however, as part of this work, only physiological signals were considered. Figure 1 from [6] shows the experimental setup that was also used in this work.  
The database consists of five parts, of which only part A was used in this study. The starting temperature for all subjects was 32\({}^{\circ}\) Celsius (this starting level is also called the global pain-free level BL1), and then to stimulate pain artificially, the temperature was slowly increased. The temperature was increased until the participants felt a change from heat to pain (pain threshold PA1). Likewise, the temperature was incremented up to a maximum threshold of PA4 when the pain became hardly bearable. Additionally, two in-between pain elicitation levels, PA2 and PA3, were calculated, making the four individual pain levels, PA1, PA2, PA3, and PA4, equidistant [6; 10]; this can be observed in Figure 2.  
Each of the pain levels was held for a total of 4s. Each pain stimulation was followed by a rest period during which the baseline temperature was held for a random duration of 8-12 s. There were 90 participants in this experiment and the three age groups were 18-35, 36-50, and 51-65 years, respectively. There were 15 male and 15 female participants in each age group. The database is available for public usage. Due to technical issues, data from 3 participants could not be collected, and hence the database consists of data from the remaining 87 participants.  
Figure 3 from [6] shows a temperature plot of a stimulus and the following pause. As part of this work, EDA signals played a vital role in pain assessment, as stated in [10], and EDA signals were primarily considered as part of this research work. Two important pain levels were BL1(when no pain was present) and PA4 (the highest level of perceived pain). The AI model was trained to be a binary classifier with two labels, BL1, indicating no pain, and PA4, indicating the presence of pain.  
Figure 1: Experimental setting with heat stimulation and recording of biopotentials and video.