Multimodal Pain Level Recognition using Majority Voting Technique

Salah, Amir; Khalil, Mahmoud; Abbas, Hazem M.

doi:10.1109/icces.2018.8639215

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Previous outcomes on the automated recognition of pain can be summarised as follows. The classification of physiological sensor data yields better results compared to the ones based on behaviour input such as video data [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Regarding physiological modalities, EDA was detected as the individual modality with the highest impact on the classification outcome [ 13 , 20 , 26 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition

Gouverneur

Shirahama

et al. 2023

Sensors

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Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.

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