Papers
4
Total Citations
267
H-Index
4
About
Preeti Bajaj is a leading researcher in motor learning, rehabilitation robotics, and biomedical microsystems, whose work bridges engineering and neuroscience to transform how we understand and enhance human movement. Her most impactful contributions center on **error augmentation**—a paradigm-shifting approach that uses robotic systems to amplify performance errors during reaching tasks, thereby accelerating motor learning and rehabilitative relearning. In her highly cited 2005 study (126 citations), she developed a real-time controller for a 2-degree-of-freedom robotic system using xPC Target, demonstrating that augmented error feedback can drive faster and more complete adaptation to novel visuomotor transformations. Her 2013 follow-up (84 citations) refined this method, showing that instantaneous trajectory error feedback promotes superior learning in healthy adults adapting to a 30° visuomotor rotation. Beyond motor control, Bajaj has explored biomedical micro- and nanotechnology, including lab-on-chip devices for rapid detection of cells, bacteria, and DNA. Her work has profound implications for stroke rehabilitation and neurorehabilitation, offering evidence-based strategies to retrain the brain through robotic assistance. With over 260 citations across her key publications, Bajaj is a pioneer in merging haptics, real-time control, and learning theory.
Research Focus
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Top Papers
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