Feed-forward neural networks improve speed and provide more accurate control of brain-controlled prosthetic hands and fingers.
Researchers have developed a new, fully automated prosthetic arm that learns during normal use and adapts to varying conditions.
Researchers have developed a novel hybrid machine learning approach to muscle gesture recognition in prosthetic arms.
A newly developed bionic arm combines motor control with touch and hand movement sensations, allowing those with upper-arm amputations to behave and react as though they haven't lost their limb.
Using magnetic beads implanted into muscle tissue within the amputated residuum of animals, researchers have created a more precise way to control prosthetic limbs.
Using virtual reality technology, researchers found the faulty alignment of prosthetics resulted in a measurable loss in performance for users.
A newly developed robotic thumb imprints how the hand is represented in the brain. Using the robotic thumb, researchers reported improvements in conducting dexterity tasks, such as building with blocks. Additionally, those who trained to use the additional thumb began to feel as though the digit was a part of their body.
After a year of using a bionic arm, patients report subjective sensations did not shift to match the location of the touch sensor on their prosthetic device.
A newly developed simultaneous brain-machine interface allowed a quadriplegic man to control two prosthetic arms with the power of his mind.
Spinal cord stimulation restores the sense of feeling of limbs lost to amputation.
A prosthetic arm that is attached to the bone and controlled by electrodes implanted in nerves and muscles can operate more precisely than conventional prosthetic limbs. Researchers improved the neuroprosthetic hand by integrating tactile sensory feedback, so the patient can "feel" items.
A bilateral implant and brain-machine interface technology allowed a patient with a high spinal cord injury to control prosthetic arms with the power of thought.
