Transactions on
Neural Systems and Rehabilitation Engineering
Highlights
Daniel P. Ferris is the Robert W. Adenbaum Professor of Engineering Innovation at the University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering. He studies how to integrate machines and humans to improve human performance and mobility in health and disability. Specific research projects focus on robotic lower limb exoskeletons, bionic lower limb prostheses, and mobile brain imaging with high-density electroencephalography. Prof. Ferris completed his B.S. from the University of Central Florida, his M.S. from the University of Miami, and his Ph.D. from University of California, Berkeley. After earning his doctoral degree, he worked as a post-doctoral researcher in the UCLA Department of Neurology and the University of Washington Department of Electrical Engineering. Dr. Ferris spent 16 years at the University of Michigan until recently relocating to the University of Florida in June 2017.
Daniel P. Ferris is the Robert W. Adenbaum Professor of Engineering Innovation at the University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering. He studies how to integrate machines and humans to improve human performance and mobility in health and disability. Specific research projects focus on robotic lower limb exoskeletons, bionic lower limb prostheses, and mobile brain imaging with high-density electroencephalography. Prof. Ferris completed his B.S. from the University of Central Florida, his M.S. from the University of Miami, and his Ph.D. from University of California, Berkeley. After earning his doctoral degree, he worked as a post-doctoral researcher in the UCLA Department of Neurology and the University of Washington Department of Electrical Engineering. Dr. Ferris spent 16 years at the University of Michigan until recently relocating to the University of Florida in June 2017.
Updates
Neural Systems and Rehabilitation Engineering
VOLUME 28
NUMBER 6
ITNSB3
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs