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Detecting tremors is challenging for both humans and machines. Infantsexposed to opioids during pregnancy often show signs and symptoms of withdrawalafter birth, which are easy to miss with the human eye. The constellation ofclinical features, termed as Neonatal Abstinence Syndrome (NAS), includetremors, seizures, irritability, etc. The current standard of care usesFinnegan Neonatal Abstinence Syndrome Scoring System (FNASS), based onsubjective evaluations. Monitoring with FNASS requires highly skilled nursingstaff, making continuous monitoring difficult. In this paper we propose anautomated tremor detection system using amplified motion signals. Wedemonstrate its applicability on bedside video of infant exhibiting signs ofNAS. Further, we test different modes of deep convolutional network basedmotion magnification, and identify that dynamic mode works best in the clinicalsetting, being invariant to common orientational changes. We propose a strategyfor discharge and follow up for NAS patients, using motion magnification tosupplement the existing protocols. Overall our study suggests methods forbridging the gap in current practices, training and resource utilization.

Little Motion, Big Results: Using Motion Magnification to Reveal Subtle Tremors in Infants