Pattern Recognition-based Myoelectric Control of Partial-Hand Prostheses
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 these muscles. The intrinsic muscles are small and sometimes damaged as a result of the injury or 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.
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 candidate 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.