Northwestern University graduate students Eric Earley and
Adenike Adewuyi explain RIC's research efforts to improve
prosthesis control for individuals who have lost part of their hand.
Project D2, R4
Objective: To develop a pattern recognition–based myoelectric control strategy for partial-hand prostheses. The goal is to allow control of multiple hand grasps while retaining the ability to move the wrist, thus providing significant improvement in prosthesis control and function for partial-hand amputees.
Loss of part of the hand—e.g., the thumb or multiple digits—impairs the ability to make functional hand-grasps; this causes severe disability. Myoelectric prostheses use surface electromyographic (EMG) signals to control joint movements. Small, lightweight myoelectric prostheses such as motorized fingers have recently become available for partial-hand amputees. However, obtaining appropriate EMG control signals remains a challenge.
Controlling partial-hand prostheses using EMG signals from remaining intrinsic hand muscles (i.e., muscles within the hand) or touch-sensitive switches has shown promise. In practice, however, it is very difficult to get good EMG control signals from intrinsic muscles, as these muscles are small and sometimes damaged as a result of injury or the amputation. Also, because the palm of the hand is very mobile it is difficult to keep electrodes in place. It is also challenging to fit individuals with a socket that is comfortable, well-suspended, and does not impair wrist motion. Thus few individuals can successfully use a myoelectric partial-hand prosthesis.
Pattern recognition–based myoelectric control methods have been used successfully to provide enhanced, intuitive control of upper-limb prostheses. In this approach, movement-specific patterns of EMG signals are deciphered by a computer algorithm and used to determine user intent.
The most frequent cause of partial-hand amputation is trauma, often a result of work- or leisure activity–related accidents or combat-related injury, thus the majority of partial-hand amputations occur in relatively young, active individuals. These individuals need prostheses that can effectively replace lost function, allowing them to maintain productive, independent lifestyles, pursue employment, and engage in active pursuits.
We will update this page periodically with project news and related interviews.
Earley EJ., Hargrove LJ., Kuiken TA., “Dual Window Pattern Recognition Classifier for Improved Partial-Hand Prosthesis Control,” Frontiers of Neuroscience, Neural Technology section, special topic: “Current challenges and new avenues in neural interfacing: from nanomaterials and microfabrication state-of-the-art, to advanced control-theoretical and signal-processing principles.” Frontiers in Neuroscience. 10:58. doi: 10.3389/fnins.2016.00058.
E. J. Earley, L. J. Hargrove, The Effect of Wrist Position and Hand-Grasp Pattern on Virtual Prosthesis Task Performance. IEEE RAS and EMBS International Conference on Biomedical Robots and Biomechatronics (BioRob); June 26-29, 2016, Singapore.
Adenike Adewuyi, an MD/PhD (MSTP) student working on the partial-hand prosthesis control project presented a poster “Pattern Recognition-Based Myoelectric Control of Partial Hand Prostheses” at a Summer School in Neuro Rehabilitation (SSNR), September 13-18, 2015 Valencia, Spain. Adenike won the poster competition and will be provided with a free pass to the 2016 International Conference on Neurorehabilitation.
Adenike Adewuyi also won a prestigious fellowship, The 2015 United Negro College Fund (UNCF) Merck Graduate Science Research Fellowship, awarded by the United Negro College Fund, Inc. and the Merck Company Foundation.
Adewuyi A, Hargrove L, and Kuiken TA. An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control, IEEE Trans Neural Syst Rehabil Eng, Epub May 6, 2015
Adenike Adewuyi: “EMG Feature Evaluation for Improved Myoelectric Control of Partial-Hand Prostheses” 7th International IEEE EMBS Neural Engineering Conference, April 22-24, 2015, Montpellier, France:
Adenike Adewuyi and Eric Earley explain the Partial-Hand Prostheses Project (February 2015)
Earley E., Adewuyi A., and Hargrove L. (poster): Optimizing Pattern Recognition-Based Control for Partial-Hand Prosthesis Application. 36th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) conference; Chicago, August 26-30, 2014
Adenike A., Hargrove L., and Kuiken TA., Towards Improving Partial Hand Prostheses: The Effects of Intrinsic Muscle EMG and Wrist Motion on Myoelectric Pattern Recognition. Myoelectric Control Symposium (MEC), Fredericton, New Brunswick, August 18-22, 2014.
Levi Hargrove (PI) presented "Improving Myoelectric Control for Individuals with Partial Hand Amputation" at RESNA,June 2014
Interview with Adenike Adewuyi: Creating Algorithms for Partial Hand Prostheses (Northwestern University Feinberg School of Medicine News Center, January 2014)
Levi Hargrove, PhD, Principal Investigator. Dr. Hargrove is director of the Neural Engineering for Prosthetics & Orthotics Lab within the Center for Bionic Medicine. He is also a Research Assistant Professor in the Departments of Physical Medicine and Rehabilitation and in Biomedical Engineering at Northwestern University. Dr. Hargrove's research interests include pattern recognition, biological signal processing, and myoelectric control of powered prostheses. He received his BSc, MSc, and PhD degrees in electrical engineering from the University of New Brunswick, Canada.
Adenike Adewuyi, BS, is an MD/PhD candidate in Northwestern's Medical Scientist Training Program and a researcher at the Center for Bionic Medicine. Her research focuses on creating algorithms for partial hand prostheses and improving control of powered, myoelectric partial hand prostheses. She earned her BS in biomedical engineering from Harvard University.
Eric Earley, MS, is a PhD student and researcher at the Center for Bionic Medicine. He received a BS in mechanical engineering from the Colorado School of Mines, Golden, CO, in 2012, and an MS in biomedical engineering from Northwestern University. His research interests include signal processing, pattern recognition, and machine learning techniques for control of myoelectric prostheses.
Additional staff members: Annie Simon, PhD, engineering manager; Virg Mazzeo, BS, research engineer.