Facial Expressions Based Automatic Pain Assessment System

Thoria Alghamdi · Gita Alaghband

38 citations0/0 files storedApplied Sciences

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Emotion and Mood Recognition Face recognition and analysis Face Recognition and Perception

Abstract

Pain assessment is used to improve patients’ treatment outcomes. Human observers may be influenced by personal factors, such as inexperience and medical organizations are facing a shortage of experts. In this study, we developed a facial expressions-based automatic pain assessment system (FEAPAS) to notify medical staff when a patient suffers pain by activating an alarm and recording the incident and pain level with the date and time. The model consists of two identical concurrent subsystems, each of which takes one of the two inputs of the model, i.e., “full face” and “the upper half of the same face”. The subsystems extract the relevant input features via two pre-trained convolutional neural networks (CNNs), using either VGG16, InceptionV3, ResNet50, or ResNeXt50, while freezing all convolutional blocks and replacing the classifier layer with a shallow CNN. The concatenated outputs in this stage is then sent to the model’s classifier. This approach mimics the human observer method and gives more importance to the upper part of the face, which is similar to the Prkachin and Soloman pain intensity (PSPI). Additionally, we further optimized our models by applying four optimizers (SGD/ADAM/RMSprop/RAdam) to each model and testing them on the UNBC-McMaster shoulder pain expression archive dataset to find the optimal combination, InceptionV3-SGD. The optimal model showed an accuracy of 99.10% on 10-fold cross-validation, thus outperforming the state-of-the-art model on the UNBC-McMaster database. It also scored 90.56% on unseen subject data. To speed up the system response time and reduce unnecessary alarms associated with temporary facial expressions, a select but effective subset of frames was inspected and classified. Two frame-selection criteria were reported. Classifying only two frames at the middle of 30-frame sequence was optimal, with an average reaction time of at most 6.49 s and the ability to avoid unnecessary alarms.

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References

2011Painful data: The UNBC-McMaster shoulder pain expression archive database 2017Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification 2016Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video 2013Pain detection through shape and appearance features 2013Automatic Pain Intensity Estimation with Heteroscedastic Conditional Ordinal Random Fields 2020Automatic facial recognition using VGG16 based transfer learning model 2021Computer aided pain detection and intensity estimation using compact CNN based fusion network

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