ArticleFrontiers in computational neuroscience2020
Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Efficient elastic tissue motions indicate general motor skill.Scientific reports · 2025Article
- Closed-loop coupling of both physiological spindle model and spinal pathways for sensorimotor control of human center-out reaching.Frontiers in computational neuroscience · 2025Article
- Anthropomorphic motion planning for multi-degree-of-freedom arms.Frontiers in bioengineering and biotechnology · 2024Review
- Optimal fibre length and maximum isometric force are the most influential parameters when modelling muscular adaptations to unloading using Hill-type muscle models.Frontiers in physiology · 2024Article
- Linking cortex and contraction-Integrating models along the corticomuscular pathway.Frontiers in physiology · 2023Article
- 'Falling heads': investigating reflexive responses to head-neck perturbations.Biomedical engineering online · 2022Article
- Costs of position, velocity, and force requirements in optimal control induce triphasic muscle activation during reaching movement.Scientific reports · 2021Article
- Editorial: Recent Trends in Morphological Computation.Frontiers in robotics and AI · 2021Article
- Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.Frontiers in robotics and AI · 2020Article
- Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy.Frontiers in robotics and AI · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.
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