| Description |
Amputation of all or part of the upper limb, whether by trauma or by disease, is a particularly debilitating condition that greatly limits function and degrades quality of life. Commercially available prosthetic arms restore some basic functions but are far from restoring full limb functionality, such as dexterous and intuitive hand motions and fingers that provide sensory feedback. More functional, multiarticulated, sensorized robotic prosthetic hands are being developed. However, the interface from the human to the robotic prosthesis is lacking. The primary goal of the work presented herein is to develop a decoding strategy for a bidirectional human-machine prosthetic arm interface. The decoding system described here is capable of deciphering intended hand movements from the user's residual nerves and/or muscles in a bidirectional setting. This work details the evolution and improvement of a decoding strategy over time as more numbers and types of electrophysiological signal sources are used as inputs. Due to its versatility, the decode strategy described here has the potential to be used widely by amputees, and may be applicable to a number of other adaptive technologies beyond prosthetic arms. I begin by showing that peripheral nerve signals from human upper-limb amputees can be measured with implanted 100-microelectrode arrays and decoded in realtime to control 5 degrees-of-freedom (DOF) of a virtual prosthetic hand. Additionally, these implanted neural electrodes were used to provide a large variety of sensations by electrical stimulation of the nerve. In the next iteration, I expand the decoding strategy to include iv signals measured from implanted electrodes in the residual muscles of human amputees. Using these electromyographic signals, I show that amputees can control 10 DOF of a virtual or robotic prosthetic hand. In the next iteration, I expand the decoding system to include electromyographic signals measured from surface electrodes. Despite the lessspecific nature of the surface electromyographic signals, I showed that 10 DOFs could still be decoded in realtime. Finally, I used the decode strategy developed herein to decode movements from the arm stump of a human congenital upper-limb amputee (3 DOFs), and from the neck and shoulder muscles of a quadriplegic human (10 DOFs) using surface electromyographic electrodes. |
