Emily Mower
Papers
4
Total Citations
120
H-Index
3
About
Emily Mower is a leading researcher in affective computing and human-robot interaction, whose work bridges the gap between synthetic characters and authentic emotional communication. Her research focuses on how robots and avatars can perceive, express, and respond to human emotions, with a particular emphasis on ambiguous and conflicting emotional cues. In her highly cited 2009 study (60 citations), Mower explored how humans perceive emotion in synthetic characters when audio-visual information is unclear, laying critical groundwork for more naturalistic human-robot interaction. She has also pioneered the use of physiological signals—such as galvanic skin response and skin temperature—to infer user engagement and emotional state during human-robot interaction (42 citations). Notably, Mower has applied these insights to socially assistive robotics for children with autism spectrum disorders, designing systems that can adapt to individual therapeutic needs. Her work on selecting emotionally salient audio-visual features has further refined how synthetic characters convey recognizable emotions. Through her interdisciplinary approach, Mower has significantly advanced our understanding of how machines can interpret and express human emotion, making her a key figure in creating more empathetic and effective robotic companions.
Research Focus
Key Achievements
Top Papers
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