Sharlene Lansiquot
Papers
1
Total Citations
17
H-Index
1
About
Sharlene Lansiquot’s research lies at the intersection of human-robot interaction, affective computing, and developmental psychology, with a focus on using autonomous systems to understand and support early childhood development. Her most-cited work, “Autonomously detecting interaction with an affective robot to explore connection to developmental ability” (2015, 17 citations), introduces an expressive robotic interface that integrates sound, color, movement, and context to elicit and autonomously detect affective responses in young children. This pioneering study explores correlations between children’s play patterns, emotional reactions, and developmental ability, offering a novel, non-invasive method for assessing cognitive and social growth. By designing robots that can sense and respond to human emotion in real time, Lansiquot bridges engineering and child psychology, creating tools that could transform early intervention strategies. Her work is notable for its interdisciplinary approach, combining machine learning, robotics, and developmental science to build empathetic technologies. With growing interest in socially assistive robots, Lansiquot’s contributions are laying the groundwork for more intuitive, adaptive systems that can support children with diverse developmental needs.
Research Focus
Key Achievements
Top Papers
- 1