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Neonatal Monitoring Based on Facial Expression Analysis

Neonatal Monitoring Based on Facial Expression Analysis

Jungong Han, Lykele Hazelhoff, Peter H.N. de With
Copyright: © 2012 |Pages: 21
ISBN13: 9781466609754|ISBN10: 1466609753|EISBN13: 9781466609761
DOI: 10.4018/978-1-4666-0975-4.ch014
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MLA

Han, Jungong, et al. "Neonatal Monitoring Based on Facial Expression Analysis." Neonatal Monitoring Technologies: Design for Integrated Solutions, edited by Wei Chen, et al., IGI Global, 2012, pp. 303-323. https://doi.org/10.4018/978-1-4666-0975-4.ch014

APA

Han, J., Hazelhoff, L., & de With, P. H. (2012). Neonatal Monitoring Based on Facial Expression Analysis. In W. Chen, S. Oetomo, & L. Feijs (Eds.), Neonatal Monitoring Technologies: Design for Integrated Solutions (pp. 303-323). IGI Global. https://doi.org/10.4018/978-1-4666-0975-4.ch014

Chicago

Han, Jungong, Lykele Hazelhoff, and Peter H.N. de With. "Neonatal Monitoring Based on Facial Expression Analysis." In Neonatal Monitoring Technologies: Design for Integrated Solutions, edited by Wei Chen, Sidarto Bambang Oetomo, and Loe Feijs, 303-323. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0975-4.ch014

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Abstract

Prematurely born infants are observed in a Neonatal Intensive Care Unit (NICU) for medical treatment. These infants are nursed in an incubator, where their vital body functions such as heart rate, respiration, blood pressure, oxygen saturation, and temperature are continuously monitored. However, the existing monitoring system is lack of the measurement for visual expression of the neonatal. Therefore, valuable information about the well being of the patient (e.g., pain and discomfort) may pass unnoticed. This chapter aims at designing a prototype of an automated video monitoring system for the detection of discomfort in newborns by analyzing their facial expression. The system consists of several algorithmic components, ranging from the face detection, ROI determination, facial feature extraction, to behavior stage classification. To further adapt this system to the real hospital environment, the authors also intend to address the problem of locating the face regions under varying lighting conditions. To this end, an adaptive face detection technique based on gamut mapping is presented. The authors have evaluated the prototype system on recordings of a healthy newborn with different conditions, and we show that our algorithm can operate with approximately 88% accuracy.

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