F
Francesco Tenore
Researcher at Johns Hopkins University Applied Physics Laboratory
Publications - 54
Citations - 2218
Francesco Tenore is an academic researcher from Johns Hopkins University Applied Physics Laboratory. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 21, co-authored 46 publications receiving 1947 citations. Previous affiliations of Francesco Tenore include Johns Hopkins University.
Papers
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Journal ArticleDOI
Decoding of Individuated Finger Movements Using Surface Electromyography
Francesco Tenore,Ander Ramos,Ahmed S. Fahmy,Soumyadipta Acharya,Ralph Etienne-Cummings,Nitish V. Thakor +5 more
TL;DR: It is shown that it is possible to decode individual flexion and extension movements of each finger with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals.
Journal ArticleDOI
Restoring the sense of touch with a prosthetic hand through a brain interface.
Gregg A. Tabot,John F. Dammann,J. A. Berg,Francesco Tenore,Jessica L Boback,R. Jacob Vogelstein,Sliman J. Bensmaia +6 more
TL;DR: This work proposes that the timing of contact events can be signaled through phasic intracortical microstimulation at the onset and offset of object contact that mimics the ubiquitous on and off responses observed in primary somatosensory cortex to complement slowly varying pressure-related feedback.
Proceedings ArticleDOI
Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals
Francesco Tenore,A. Ramos,Ahmed S. Fahmy,Soumyadipta Acharya,Ralph Etienne-Cummings,Nitish V. Thakor +5 more
TL;DR: A framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy and extended to non-invasive control of the next generation of upper-limb prostheses for amputees is presented.
Proceedings ArticleDOI
Continuous decoding of finger position from surface EMG signals for the control of powered prostheses
TL;DR: A basis for asynchronous decoding of finger positions is developed through the use of generalized electrode placement and well-established methods of pattern recognition to encourage further investigation into more intuitive methods of myoelectric control of powered upper limb prostheses.
Proceedings ArticleDOI
Low-cost electroencephalogram (EEG) based authentication
TL;DR: The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs) and achieve 100% classification accuracy for each subject in each task, and shows that low-cost EEG authentication systems may be viable.