Browsing by Subject "Visual analogue scale"

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Open Access
Enforcing multilabel consistency for automatic spatio-temporal assessment of shoulder pain intensity
(Association for Computing Machinery, 2020) Erekat, Diyala; Hammal, Z.; Siddiqui, M.; Dibeklioğlu, Hamdi
The standard clinical assessment of pain is limited primarily to self-reported pain or clinician impression. While the self-reported measurement of pain is useful, in some circumstances it cannot be obtained. Automatic facial expression analysis has emerged as a potential solution for an objective, reliable, and valid measurement of pain. In this study, we propose a video based approach for the automatic measurement of self-reported pain and the observer pain intensity, respectively. To this end, we explore the added value of three self-reported pain scales, i.e., the Visual Analog Scale (VAS), the Sensory Scale (SEN), and the Affective Motivational Scale (AFF), as well as the Observer Pain Intensity (OPI) rating for a reliable assessment of pain intensity from facial expression. Using a spatio-temporal Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) architecture, we propose to jointly minimize the mean absolute error of pain scores estimation for each of these scales while maximizing the consistency between them. The reliability of the proposed method is evaluated on the benchmark database for pain measurement from videos, namely, the UNBC-McMaster Pain Archive. Our results show that enforcing the consistency between different self-reported pain intensity scores collected using different pain scales enhances the quality of predictions and improve the state of the art in automatic self-reported pain estimation. The obtained results suggest that automatic assessment of selfreported pain intensity from videos is feasible, and could be used as a complementary instrument to unburden caregivers, specially for vulnerable populations that need constant monitoring.
Open Access
Spatio-temporal assessment of pain intensity through facial transformation-based representation learning
(2021-09) Erekat, Diyala Nabeel Ata
The nature of pain makes it di cult to assess due to its subjectivity and multidimensional characteristics that include intensity, duration, and location. However, the ability to assess pain in an objective and reliable manner is crucial for adequate pain management intervention as well as the diagnosis of the underlying medical cause. To this end, in this thesis, we propose a video-based approach for the automatic measurement of self-reported pain. The proposed method aims to learn an e cient facial representation by exploiting the transformation of one subject's facial expression to that of another subject's within a similar pain group. We also explore the e ect of leveraging self-reported pain scales i.e., the Visual Analog Scale (VAS), the Sensory Scale (SEN), and the A ective Motivational Scale (AFF), as well as the Observer Pain Intensity (OPI) on the reliable assessment of pain intensity. To this end, a convolutional autoencoder network is proposed to learn the facial transformation between subjects. The autoencoder's optimized weights are then used to initialize the spatio-temporal network architecture, which is further optimized by minimizing the mean absolute error of estimations in terms of each of these scales while maximizing the consistency between them. The reliability of the proposed method is evaluated on the benchmark database for pain measurement from videos, namely, the UNBC-McMaster Pain Archive. Despite the challenging nature of this problem, the obtained results show that the proposed method improves the state of the art, and the automated assessment of pain severity is feasible and applicable to be used as a supportive tool to provide a quantitative assessment of pain in clinical settings.