Papers
4
Total Citations
31
H-Index
3
About
Isabelle M. Shuggi’s research lies at the intersection of motor learning, cognitive workload, and human-robot interaction, with a focus on how individuals acquire and refine reaching movements. Her work uniquely integrates team dynamics theory and neurophysiological measures to understand how mental workload and self-efficacy evolve across multiple practice sessions. In her most-cited study (2019, 16 citations), she demonstrated that motor performance and mental workload follow distinct, non-linear trajectories during learning, offering a dynamic perspective on skill acquisition. Her 2017 paper (10 citations) further explored these changes through a team dynamics lens, revealing how cognitive and motor systems coordinate during practice. Shuggi has also contributed to human-robotic control systems (2013, 4 citations), designing intelligent interfaces for reaching performance. Most recently (2025), she has pioneered the use of brain biomarkers to assess practice of robotic arm reaching movements via head-controlled interfaces, addressing a critical gap in understanding cerebral cortical dynamics during assistive device use. Her work has direct implications for rehabilitation engineering and the design of adaptive prosthetics, making her a key voice in cognitive-motor neuroscience and human-machine collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Human-Robotic Collaborative Intelligent Control for Reaching Performance4 citations · 2013
- 4