TY - JOUR
T1 - High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm
AU - Ison, Mark
AU - Vujaklija, Ivan
AU - Whitsell, Bryan
AU - Farina, Dario
AU - Artemiadis, Panagiotis
N1 - Funding Information:
Manuscript received October 31, 2014; revised January 19, 2015 and March 06, 2015; accepted March 22, 2015. Date of publication March 31, 2015; date of current version April 06, 2016. This work was supported by the European Research Council (ERC) via the ERC Advanced under Grant DEMOVE 267888. All authors declare no conflict of interests.
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2016/4
Y1 - 2016/4
N2 - Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.
AB - Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.
KW - Electromyography (EMG)
KW - high-density electromyography (EMG)
KW - human-robot interaction
KW - motor learning
KW - myoelectric control
KW - prosthetics
KW - real-time systems
KW - simultaneous control
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U2 - 10.1109/TNSRE.2015.2417775
DO - 10.1109/TNSRE.2015.2417775
M3 - Article
C2 - 25838524
AN - SCOPUS:84963788708
SN - 1534-4320
VL - 24
SP - 424
EP - 433
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 4
M1 - 7073629
ER -