Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands

• Published in: IEEE transactions on robotics Vol. 38; no. 5; pp. 2841 - 2857
• Main Authors: Vaskov, Alex K., Vu, Philip P., North, Naia, Davis, Alicia J., Kung, Theodore A., Gates, Deanna H., Cederna, Paul S., Chestek, Cynthia A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classifiers; Electrodes; Electromyography; Finger and grasp control; Hand (anatomy); Hidden Markov models; High speed; Implanted electrodes (biology); intramuscular electrodes; Muscles; myoelectric prostheses; Pattern recognition; Performance assessment; peripheral nerve interfaces; Peripheral nerves; Prostheses; Real time; Robots; Switches; Task analysis; Wrist; myoelectric prostheses; peripheral nerve interfaces; finger and grasp control; intramuscular electrodes
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2022.3170720

Currently available prosthetic hands are capable of actuating anywhere from five to 30 degrees of freedom. However, grasp control of these devices remains unintuitive and cumbersome. To address this issue, we propose directly extracting finger commands from the neuromuscular system. Two persons with transradial amputations had bipolar electrodes implanted into regenerative peripheral nerve interfaces (RPNIs) and residual innervated muscles. The implanted electrodes recorded local electromyography with large signal amplitudes. In a series of single-day experiments, participants used a high-speed movement classifier to control a virtual prosthetic hand in real time. Both participants transitioned between ten pseudorandomly cued individual finger and wrist postures with an average success rate of 94.7% and trial latency of 255 ms. When the set was reduced to five grasp postures, metrics improved to 100% success and 135 ms trial latency. Performance remained stable across untrained static arm positions while supporting the weight of the prosthesis. Participants also used the high-speed classifier to switch between robotic prosthetic grips and complete a functional performance assessment. These results demonstrate that pattern-recognition systems can use intramuscular electrodes and RPNIs for fast and accurate prosthetic grasp control.