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Jeong, D., Kim, B.-G. and Dong, S.-Y. (2020) Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition. Sensors, 20, Article No. 1936.
https://doi.org/10.3390/s20071936

has been cited by the following article:

• TITLE: Analysis of Emotions Using Multimodal Data: A Case Study

  AUTHORS: Toshiya Akiyama, Kyoko Osaka, Hirokazu Ito, Ryuichi Tanioka, Allan Paulo Blaquera, Leah Anne Christine Bollos, Tetsuya Tanioka

  KEYWORDS: Humanoid Robots, Multimodal Data, Emotion Analysis

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.11 No.12, December 8, 2023

  ABSTRACT: In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency