Dynamic primitives in the control of locomotion
Name
Hogan-2013-Dynamic primitives in the control of locomotion.pdf
Size
3.01 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Hogan, Neville
Sternad, Dagmar
Date Issued
June 2013
Journal
Frontiers in Computational Neuroscience
Publisher
Frontiers Research Foundation
Citation
Hogan, Neville, and Dagmar Sternad. “Dynamic Primitives in the Control of Locomotion.” Frontiers in Computational Neuroscience 7 (2013).
Version
Final published version
Abstract
Humans achieve locomotor dexterity that far exceeds the capability of modern robots, yet this is achieved despite slower actuators, imprecise sensors, and vastly slower communication. We propose that this spectacular performance arises from encoding motor commands in terms of dynamic primitives. We propose three primitives as a foundation for a comprehensive theoretical framework that can embrace a wide range of upper- and lower-limb behaviors. Building on previous work that suggested discrete and rhythmic movements as elementary dynamic behaviors, we define submovements and oscillations: as discrete movements cannot be combined with sufficient flexibility, we argue that suitably-defined submovements are primitives. As the term “rhythmic” may be ambiguous, we define oscillations as the corresponding class of primitives. We further propose mechanical impedances as a third class of dynamic primitives, necessary for interaction with the physical environment. Combination of these three classes of primitive requires care. One approach is through a generalized equivalent network: a virtual trajectory composed of simultaneous and/or sequential submovements and/or oscillations that interacts with mechanical impedances to produce observable forces and motions. Reliable experimental identification of these dynamic primitives presents challenges: identification of mechanical impedances is exquisitely sensitive to assumptions about their dynamic structure; identification of submovements and oscillations is sensitive to their assumed form and to details of the algorithm used to extract them. Some methods to address these challenges are presented. Some implications of this theoretical framework for locomotor rehabilitation are considered.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Newman Laboratory for Biomechanics and Human Rehabilitation
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.3389/fncom.2013.00071
