@article{ref1,
title="Quantitative evaluation of facial expression in a patient with minimally conscious state after severe traumatic brain injury",
journal="Journal of head trauma rehabilitation",
year="2021",
author="Obayashi, Yota and Uehara, Shintaro and Kokuwa, Ryu and Otaka, Yohei",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: To investigate whether automatic facial expression analysis can quantify differences in the intensity of facial responses depending on the affective stimuli in a patient with minimally conscious state (MCS). <br><br>METHODS: We filmed the facial responses of a patient with MCS during the delivery of three 1-minute auditory stimuli: audio clips of comedy movies, a nurse hilariously talking, and recitation of a novel (comedy, nurse, and recitation conditions, respectively). These measures were repeated at least 13 times for each condition on different days for approximately 10 months. The intensity of being &quot;happy&quot; was estimated from the smiling face using a software called FaceReader. The intensity among 5 conditions including those at 2 resting conditions (pre- and poststimuli) was compared using the Kruskal-Wallis test and the Dunn-Bonferroni test for multiple comparisons. <br><br>RESULTS: Significantly higher values were found in the intensity of being &quot;happy&quot; in the comedy and nurse conditions versus other conditions, with no significant differences between the recitation and pre- or poststimulus conditions. These findings indicate that the automated facial expression analysis can quantify differences in context-dependent facial responses in the patient recruited in this study. <br><br>CONCLUSIONS: This case study demonstrates the feasibility of using automated facial expression analysis to quantitatively evaluate the differences in facial expressions and their corresponding emotions in a single patient with MCS.<p /> <p>Language: en</p>",
language="en",
issn="0885-9701",
doi="10.1097/HTR.0000000000000666",
url="http://dx.doi.org/10.1097/HTR.0000000000000666"
}