Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human–machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.

is a lecturing professor with the Mechanical Engineering department at Northwestern University. His research was conducted at the intersection of robotics and biomechanics, in the field of human-machine interactions, and explored novel ways to control robotic prosthetic hands. He is very passionate about student education and currently teaches five separate courses at the undergraduate level that include manufacturing, design, experimental methods, and thermodynamics. He greatly enjoys advising all levels of undergraduate and early graduate students. He is the producer for the Lightboard studio, and is currently exploring models for effective online and hybrid teaching models.

is an assistant professor of instruction with the Undergraduate Engineering Office and the Mechanical Engineering department at Northwestern University. His research explored novel ways to control robotic prosthetic hands. He is very passionate about student education and has taught multiple courses at the undergraduate level that include manufacturing, freshman and capstone design, experimental methods, and thermodynamics. He greatly enjoys advising all levels of undergraduate and early graduate students. He has been highly involved with the Lightboard studio and exploring models for effective online and hybrid teaching methods.

Alex Birdwell is an assistant professor of instruction with the Undergraduate Engineering Office and the Mechanical Engineering department at Northwestern University. His research was conducted at the intersection of robotics and biomechanics in the field of human-machine interactions, and explored novel ways to control robotic prosthetic hands. He is very passionate about student education and currently teaches courses at the undergraduate level that have included manufacturing, design, experimental methods, and thermodynamics. He greatly enjoys advising all levels of undergraduate engineering, but predominantly works with first-year students in his role as a McCormick Advisor. He is the producer for the Lightboard studio, and is currently exploring models for effective online and hybrid teaching models. Dr. Ken Gentry, Northwestern UniversityKen Gentry is a Senior Lecturer and Adviser working mainly with first-year students. He teaches cornerstone design and courses in the biomedical engineering department.