Papers
1
Total Citations
7
H-Index
1
About
Julie Golliot is a researcher focused on the intersection of robotics and early childhood developmental diagnostics, with a particular emphasis on autism spectrum disorder. Her most notable contribution is the development of QueBall, a spherical robot designed to assist in diagnosing autism in children aged two to five. This innovative tool integrates motion, touch sensors, multi-colored lights, sounds, and wireless connectivity with iOS devices, offering a novel, engaging platform for early screening. Although her foundational paper on this work has garnered 7 citations, its significance lies in laying the groundwork for future research into robotic-assisted diagnosis. Golliot’s work highlights the potential of interactive technology to create standardized, non-invasive diagnostic protocols, addressing a critical gap in early autism detection. Her research bridges child psychology, human-robot interaction, and assistive technology, paving the way for more accessible and objective screening methods. By proposing a tangible tool for clinicians, Golliot has opened new avenues for leveraging robotics in pediatric healthcare, inspiring further exploration into how sensory-rich robots can support developmental assessments.
Research Focus
Key Achievements
Top Papers
- 1A Tool to Diagnose Autism in Children Aged Between Two to Five Old7 citations · 2015