Papers
8
Total Citations
507
H-Index
5
About
Michelle Karg is a researcher whose work bridges affective computing, human motion analysis, and human-robot interaction. Her research focuses on understanding how the human body — particularly gait and movement — communicates emotional and affective states, and on translating these insights into computational models for robots and virtual agents. Karg's most influential contribution is her 2013 survey on body movements for affective expression, which has garnered over 217 citations and remains a landmark reference for researchers working on automatic recognition and generation of emotionally expressive movement. Her earlier work on gait-based affect recognition (2010, 151 citations) demonstrated that a person's emotional state could be inferred from walking patterns alone, advancing the possibility of emotion detection at a distance — a significant practical achievement. Her 2016 framework for movement primitive segmentation (106 citations) further expanded the field by providing systematic tools for decomposing and modeling complex human motion sequences, with applications spanning exercise monitoring, gesture recognition, and human-machine interaction. Beyond recognition, Karg explored how emotions could be mapped onto robotic motion, striving for more natural and believable human-robot interaction. Her doctoral research synthesized these threads into a coherent vision of nonverbal emotional communication in robotics, establishing her as a foundational voice in embodied affective computing.
Research Focus
Key Achievements
Top Papers
- 1
- 2Recognition of Affect Based on Gait Patterns151 citations · 2010
- 3
- 4Towards mapping emotive gait patterns from human to robot13 citations · 2010
- 5IMU based single stride identification of humans8 citations · 2013
- 6
- 7Physiology and HRI: Recognition of over- and underchallenge4 citations · 2008
- 8