The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space
Article
Article Title | The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space |
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ERA Journal ID | 17759 |
Article Category | Article |
Authors | Bargshady, Ghazal (Author), Zhou, Xujuan (Author), Deo, Ravinesh C. (Author), Soar, Jeffrey (Author), Whittaker, Frank (Author) and Wang, Hua (Author) |
Journal Title | Applied Soft Computing |
Journal Citation | 97 (Part A), pp. 1-14 |
Article Number | 106805 |
Number of Pages | 14 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1568-4946 |
1872-9681 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2020.106805 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1568494620307432 |
Abstract | An accurate detection and management of pain, measured through its relative intensity, plays an important role in the treatment of disease and reducing a patient’s discomfort. As it is relatively difficult to assess, describe, evaluate and manage the pain level using a patient’s self-report, automated pain-detecting tools can provide useful information to assist in the management of pain intensity. This study proposes a new predictive modeling framework that employs a modified Temporal Convolutional Network (TCN) algorithm to recognize the pain intensity prevalent in patients’ video frames collected as part of UNBC-McMaster Shoulder Pain Archive and MIntPAIN databases. The inputs of the proposed TCN network is composed of the extracted and reduced face image features from a fine-tuned VGG-Face and principal component analysis (PCA) with Hue, Saturation, Value (HSV) color spaces video images. The results of TCN based predictive model, employing a long short-term memory (LSTM) model as well as other state-of-the art models, show that the proposed approach performs faster with a high level of efficiency. This is demonstrated by the low magnitude of error metrics (i.e., Mean Squared Error = 0.0629, Mean Absolute Error = 0.1021, correctness validation results represented by Area under Curve = 85% and accuracy metric = 92.44%). Considering the efficiency of the proposed TCN framework, integrating fine-tuned VGG-Face and PCA with Hue, Saturation, Value (HSV) color spaces video images for pain intensity estimation, the present study affirms that the new method can be adopted as an automatic health informatics tool, mainly for pain detection, and subsequently, implemented in the pain management area. |
Keywords | Temporal convolutional network; Facial expression; Pain detection; HSV color space; Video analysis |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
469999. Other information and computing sciences not elsewhere classified | |
460304. Computer vision | |
490502. Biostatistics | |
460306. Image processing | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | School of Management and Enterprise |
School of Management and Enterprise | |
School of Sciences | |
Victoria University | |
Funding source | Australian Research Council (ARC) Grant ID LP150100673 |
https://research.usq.edu.au/item/q5z08/the-modeling-of-human-facial-pain-intensity-based-on-temporal-convolutional-networks-trained-with-video-frames-in-hsv-color-space
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