Adaptive Auto-Regressive Proportional Myoelectric Control

In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine s...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2019-02, Vol.27 (2), p.314-322
Main Authors: Igual, Carles, Igual, Jorge, Hahne, Janne M., Parra, Lucas C.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive filters
Adaptive linear filtering
Adaptive systems
Adult
Algorithms
Biomechanical Phenomena
Brain
control
Electromyography
Electromyography - methods
Feedback
Female
Filters
Humans
Least-Squares Analysis
Machine Learning
Male
Man-Machine Systems
Motion control
Muscles
Myoelectric control
Myoelectricity
Proportional control
prosthesis
Prosthetics
Psychomotor Performance
recursive least squares
Velocity
Velocity control
Wrist
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Summary:In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a 2-D arena. We tested this on 15 able-bodied and three limb-deficient participants using an eight-channel myoelectric armband. The human-machine pairs learn to perform smoother cursor movements with a larger range of motion when using the auto-regressive filters, as compared with our previous efforts with moving-average filters. Importantly, the human-machine system converges to an approximate velocity control strategy resulting in faster and more accurate movements with less muscle effort. The method is not specific to myoelectric control and could be used equally well for motion control using high-dimensional signals from reinnervated muscles or direct brain recordings.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2019.2894464