ArticleScientific reports2018
An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Human arm redundancy: a new approach for the inverse kinematics problem.Royal Society open science · 2024Article
- A trade-off between kinematic and dynamic control of bimanual reaching in virtual reality.Journal of neurophysiology · 2022Article
- Analysis of Visuo Motor Control between Dominant Hand and Non-Dominant Hand for Effective Human-Robot Collaboration.Sensors (Basel, Switzerland) · 2020Article
- An inverse optimization approach to understand human acquisition of kinematic coordination in bimanual fine manipulation tasks.Biological cybernetics · 2020Article
- Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics.Frontiers in computational neuroscience · 2020Article
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Authors and funding
4 authors.
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Abstract
Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.
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