Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions
Summary :
The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.7 pp.1860-1869
- Publication Date
- 2018/07/01
- Publicized
- 2018/04/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7272
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Ruicong ZHI
University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Ghada ZAMZMI
University of South Florida
Dmitry GOLDGOF
University of South Florida
Terri ASHMEADE
University of South Florida
Tingting LI
University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Yu SUN
University of South Florida
Keyword
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Ruicong ZHI, Ghada ZAMZMI, Dmitry GOLDGOF, Terri ASHMEADE, Tingting LI, Yu SUN, "Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 7, pp. 1860-1869, July 2018, doi: 10.1587/transinf.2017EDP7272.
Abstract: The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7272/_p
Copy
@ARTICLE{e101-d_7_1860,
author={Ruicong ZHI, Ghada ZAMZMI, Dmitry GOLDGOF, Terri ASHMEADE, Tingting LI, Yu SUN, },
journal={IEICE TRANSACTIONS on Information},
title={Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions},
year={2018},
volume={E101-D},
number={7},
pages={1860-1869},
abstract={The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).},
keywords={},
doi={10.1587/transinf.2017EDP7272},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions
T2 - IEICE TRANSACTIONS on Information
SP - 1860
EP - 1869
AU - Ruicong ZHI
AU - Ghada ZAMZMI
AU - Dmitry GOLDGOF
AU - Terri ASHMEADE
AU - Tingting LI
AU - Yu SUN
PY - 2018
DO - 10.1587/transinf.2017EDP7272
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2018
AB - The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).
ER -
English
