ArticleFrontiers in computational neuroscience2013
Dynamic primitives in the control of locomotion.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Using an unbiased symbolic movement representation to characterize Parkinson's disease states.Scientific reports · 2020Trial
- Emergent motor timing enhances time perception.iScience · 2025Article
- Quantifying spontaneous infant movements using state-space models.Scientific reports · 2024Article
- Robot Learning Method for Human-like Arm Skills Based on the Hybrid Primitive Framework.Sensors (Basel, Switzerland) · 2024Article
- Dynamic primitives in constrained action: systematic changes in the zero-force trajectory.Journal of neurophysiology · 2024Article
- Article
- Body Mechanics, Optimality, and Sensory Feedback in the Human Control of Complex Objects.Neural computation · 2023Article
- Article
- Biomarkers for rhythmic and discrete dynamic primitives in locomotion.Scientific reports · 2022Article
- Combined Gait and Grasping in Autistic and Non-Autistic Youths.Developmental neurorehabilitation · 2022Article
- How to improve the muscle synergy analysis methodology?European journal of applied physiology · 2021Review
- Identifying human postural dynamics and control from unperturbed balance.Journal of neuroengineering and rehabilitation · 2021Article
- Closing the Loop on Exoskeleton Motor Controllers: Benefits of Regression-Based Open-Loop Control.IEEE robotics and automation letters · 2020Article
- 3D human arm reaching movement planning with principal patterns in successive phases.Journal of computational neuroscience · 2020Article
- Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions.Journal of neurophysiology · 2020Article
- Article
- Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework.Frontiers in neurorobotics · 2020Article
- Highlights from the 29th Annual Meeting of the Society for the Neural Control of Movement.Journal of neurophysiology · 2019Article
- Control of goal-directed movements within (or beyond) reach?: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by Vishwanathan Mohan et al.Physics of life reviews · 2019Article
- The primacy of rhythm: how discrete actions merge into a stable rhythmic pattern.Journal of neurophysiology · 2019Article
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2 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.