An Approach for Automated Multimodal Analysis of Infants' Pain
pain, pediatrics, strain, feature extraction, physiology, biomedical monitoring, optical imaging, automated multimodal analysis, physiological pain indicators, infant pain assessment, behavioral pain indicators, facial expression, body movement
Digital Object Identifier (DOI)
Current practices of assessing infants' pain depends on the observer's subjective and potentially inconsistent judgment and requires continuous monitoring by care providers. Therefore, pain may be misinterpreted or totally missed leading to misdiagnosis and over/under treatment. To address these shortcomings, current practices can be augmented with a machine-based assessment system that monitors various pain cues and provides an objective and continuous assessment of pain. Although several machine-based pain assessment approaches have been introduced, the majority of these approaches assess pain based on analysis of a single pain indicator (i.e., unimodal). In this paper, we propose an automated multimodal approach that utilizes a combination of both behavioral and physiological pain indicators to assess infants' pain. We also present a unimodal approach that depends on a single pain indicator for assessment. Recogsnizing pain using a single indicator yielded 88%, 85%, and 82% overall accuracies for facial expression, body movement, and vital signs, respectively. Combining facial expression, body movement, and changes in vital signs (i.e., the multimodal approach) for assessment achieved 95% overall accuracy. These preliminarily results indicate that utilizing both behavioral and physiological pain indicators could provide a better and more reliable assessment of infants' pain.
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Citation / Publisher Attribution
23rd International Conference on Pattern Recognition (ICPR), Cancun, 2016, p. 4148-4153.
Scholar Commons Citation
Zamzmi, Ghada; Pai, Chih-Yun; Goldgof, Dmitry; Kasturi, Rangachar; Ashmeade, Terri; and Sun, Yu, "An Approach for Automated Multimodal Analysis of Infants' Pain" (2016). Computer Science and Engineering Faculty Publications. 83.