  Detecting tremors is challenging for both humans and machines. Infants
exposed to opioids during pregnancy often show signs and symptoms of withdrawal
after birth, which are easy to miss with the human eye. The constellation of
clinical features, termed as Neonatal Abstinence Syndrome (NAS), include
tremors, seizures, irritability, etc. The current standard of care uses
Finnegan Neonatal Abstinence Syndrome Scoring System (FNASS), based on
subjective evaluations. Monitoring with FNASS requires highly skilled nursing
staff, making continuous monitoring difficult. In this paper we propose an
automated tremor detection system using amplified motion signals. We
demonstrate its applicability on bedside video of infant exhibiting signs of
NAS. Further, we test different modes of deep convolutional network based
motion magnification, and identify that dynamic mode works best in the clinical
setting, being invariant to common orientational changes. We propose a strategy
for discharge and follow up for NAS patients, using motion magnification to
supplement the existing protocols. Overall our study suggests methods for
bridging the gap in current practices, training and resource utilization.
