Distance Ordering: A Deep Supervised Metric Learning for Pain Intensity Estimation
Journal
Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Pages
1083-1088
ISBN
9781665443371
Date Issued
2021-01-01
Author(s)
Abstract
The pain intensity level is one of the properties to know the patients' status. In this work, we focus on estimating each individual's self-reported pain metric called Visual Analogue Scale (VAS), which is considered the standard gold measurement in the triage system. The VAS pain score is highly subjective, and its range may vary significantly among different patients. To tackle these issues, we designed an end-to-end training deep learning model for the automatic measurement of VAS based on video facial recognition. We proposed a novel loss method named Distance Ordering. By using Distance Ordering, we can extract the features with ordinal meaning according to the ordinal relationship of pain intensity levels. Experimental results on the UNBC-McMaster Pain Archive Database benchmark show that the model we designed outperforms the other previous works and achieves the state-of-the-art performance with Mean Square Error (MSE), Mean Absolute Error (MAE), Intra-class correlation (ICC), and Pearson coefficient correlation (PCC). Also, the ablation studies demonstrate that our approach can improve the VAS estimation.
Event(s)
20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Subjects
Deep learning | Facial Expression | Pain | Video Recognition | Visual Analogue Scale
Type
conference paper