Simultaneous wrist/hand control (where a person simultaneously moves his/her wrist while grasping or releasing), is essential in activities of daiy living, but cannot be performed by amputees using clinical surface electromyography (sEMG) based neural interfaces (myoelectric control). Transradial amputees, who make up 40% of major amputations (proximal to the wrist), cite simultaneous control as a necessary element to improve upper-limb prostheses. Our long-term goal is to develop a clinically viable EMG-based neural interface for transradial amputees that is suitable for restoring simultaneous control of prosthetic wrist/hand movements. One proposed method for intuitive simultaneous wrist/hand prosthesis control uses intramuscular EMG (imEMG) amplitude estimates from multiple agonist/ antagonist forearm muscle pairs to control physiologically appropriate degrees of freedom DOFs (e.g. flexor digitorum profundus / extensor digitorum control grasp/release). Known as 'direct control', this approach does not require burdensome levels of machine learning algorithm training, as is required for experimental sEMG-based simultaneous control methods. However, this method assumes independently modulated muscle activation patterns for each DOF - that the activation patterns of muscles controlling a DOF (i.e. finger flexors/extensors for grasp/release) do not change when a second DOF is simultaneously attempted (simultaneous pronation/supination and grasp/release). The biologic foundation of such an assumption has not been explored to date. Little is known of how the central nervous system (CNS) coordinates wrist and extrinsic hand muscle activation when independent of biomechanical effects and joint position feedback. Muscle activation patterns under such conditions are particularly relevant to amputee populations, who lack distal joints. Therefore, the objective of this research is to better understand how the CNS coordinates muscle activation patterns in healthy individuals to produce simultaneous wrist/grasp torques when independent of the biomechanical effects of joint movement. We will then apply this information to develop an imEMG simultaneous myoelectric prosthesis controller. imEMG patterns will be measured as healthy subjects produce single-DOF and simultaneous multi-DOF (SMD) wrist and/or grasp/release torques in isometric, neutral posture conditions. Multivariate models predicting imEMG activity from measured wrist/grasp torques will be developed and will suggest which DOFs have independently modulated muscle activation patterns. This information will then allow for the development of a 'biologically-inspired'simultaneous controller. DOFs with independently modulated muscle activation patterns will be controlled using direct control, while experimental pattern recognition algorithms currently used for sEMG-based simultaneous control will be used for all other DOFs. We will evaluate the performance of this hybrid controller in healthy subjects both offline and using online functional tests in a virtual environment.

Public Health Relevance

Various current prosthetic arms use the electrical signals produced when muscles contract to control prosthesis movement, but cannot provide simultaneous control of both the wrist and the hand. This study will investigate how the nervous system activates muscles to simultaneously control the wrist and hand in healthy individuals. This information can then be used to provide clinically viable methods of controlling simultaneous wrist and hand movements of prostheses to amputees.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Predoctoral Individual National Research Service Award (F31)
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NST-2 Subcommittee (NST)
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Ludwig, Kip A
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Northwestern University at Chicago
Biomedical Engineering
Schools of Engineering
United States
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Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J (2014) Real-time simultaneous and proportional myoelectric control using intramuscular EMG. J Neural Eng 11:066013
Smith, Lauren H; Hargrove, Levi J (2013) Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification. Conf Proc IEEE Eng Med Biol Soc 2013:4223-6