Sarah E. Goodman
Papers
2
Total Citations
9
H-Index
2
About
Sarah E. Goodman is a researcher in sensorimotor control and motor learning, with a focus on how humans adapt to and exploit interactive dynamics. Her work bridges computational modeling and experimental neuroscience to uncover the principles underlying how we learn to control our movements. In her most-cited study, "Learning to shape virtual patient locomotor patterns" (2018, 6 citations), she demonstrated that humans develop internal models that map neural commands to limb motion, adapting these representations to exploit the dynamics of virtual environments. This work advances our understanding of sensorimotor adaptation in complex, interactive contexts. In "Elucidating Sensorimotor Control Principles with Myoelectric Musculoskeletal Models" (2017, 3 citations), she pioneered the use of myoelectric musculoskeletal models (MMMs)—which sample motor commands via electromyography—as a tool to expand frontiers in sensorimotor control and learning. Her research has implications for rehabilitation robotics, prosthetics, and virtual reality training. Goodman’s work is notable for its innovative integration of musculoskeletal modeling with real-time neural signals, offering new insights into how the brain learns and controls movement.
Research Focus
Key Achievements
Top Papers
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- 2