University of South Florida

Ghadh A Alzamzmi

Dmitry B Goldgof

Yu Sun

Rangachar Kasturi

Terri Ashmeade

Md Sirajus Salekin

Peter Randolph Mouton

Rahul Paul

Chih-Yun Pai

Thao Ho

Sammie Lee Elkins

A Neonatal CNN (N-CNN) is provided for detecting neonatal pain emotion based upon facial recognition. A cascaded N-CNN is trained using a Neonatal Pain Assessment Database (NPAD) to automatically identify a neonatal patient experience pain in real-time. These results show that the automatic recognition of neonatal pain provided by the embodiments of the present invention is a viable and more efficient alternative to the current standard of pain assessment.

Neonatal pain identification from neonatal facial expressions

A computer-based system and method for generating a current pain assessment of a neonate using facial expressions along with crying sounds, body movement, and vital signs changes and for using the current pain objective assessment to predict future pain objective assessment and assign a future pain probability score by incorporation spatiotemporal data into the multimodal assessment.

System and method for multimodal spatiotemporal pain assessment

A system and method of automatically assessing pediatric and neonatal pain using facial expressions along with crying sounds, body movement, and vital signs change to improve the diagnosis and treatment of pain in the pediatric patient population.

Comprehensive and context-sensitive neonatal pain assessment system and methods using multiple modalities

A system and method for measuring an infant's pain intensity is presented. The method for assessing an infant's pain intensity based on facial expressions is comprised of three main stages: detection of an infant's face in video sequence followed by preprocessing operations including face alignment; expression segmentation; and expression recognition or classification. Also presented is a multimodal system for assessing an infant's pain intensity using the following classifiers: facial expression classifier; vital sign classifier; crying recognition classifier; body motion classifier and state of arousal classifier. Each classifier generates an individual score, all of which are normalized and weighed to generate a total pain score that indicates pain intensity.

Machine-based infants pain assessment tool

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Tampa, FL, United States of America

Ghadh A Alzamzmi