Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment

By David W. Franklin, G. Liaw, Theodore E. Milner, Rieko Osu, Etienne Burdet, Mitsuo Kawato. Journal of Neuroscience, 2007

327 citations recorded by the source corpus.

It has been shown that humans are able to selectively control the endpoint impedance of their arms when moving in an unstable environment. However, directional instability was only examined for the case in which the main contribution was from coactivation of biarticular muscles. The goal of this study was to examine whether, in general, the CNS activates the sets of muscles that contribute to selective control of impedance in particular directions. Subjects performed reaching movements in three differently oriented unstable environments generated by a robotic manipulandum. After subjects had learned to make relatively straight reaching movements in the unstable force field, the endpoint stiffness of the limb was measured at the midpoint of the movements. For each force field, the endpoint stiffness increased in a specific direction, whereas there was little change in stiffness in the orthogonal direction. The increase in stiffness was oriented along the direction of instability in the environment, which caused the major axis of the stiffness ellipse to rotate toward the instability in the environment. This study confirms that the CNS is able to control the endpoint impedance of the limbs and selectively adapt it to the environment. Furthermore, it supports the idea that the CNS incorporates an impedance controller that acts to ensure stability, reduce movement variability, and reduce metabolic cost.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/3d2417e2382fd2f9d25c6a67bcbd470eb0d2f533

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Citations in the corpus

Principles of sensorimotor learning (2011, background): “Not only can people scale their overall hand stiffness, or compliance, but they can also shape the pattern of stiffness either by varying muscle activations or the posture of the arm 36,37 .”
Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions (2011, background): “However, human beings use a different strategy to deal with instability [6], [7]: They adapt endpoint force and viscoelasticity to compensate for the environment forces and instability by minimizing error and effort and ensuring a constant stability margin [8], [9].”
Progress and prospects of the human–robot collaboration (2017, unknown): “However, these feedforward signals alone are not sufﬁcient in case of unstable dynamic environments: in several studies [Franklin et al., 2008,Franklin et al., 2007,Franklin et al., 2003], Franklin et al. reported evidence that the CNS regulates the arm compliance by co-contracting the muscles to…”
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm (2008, methodology): “, 2001; Singh and Scott, 2003) and feedback (Todorov and Jordan, 2002; Franklin et al., 2007; Kurtzer et al., 2008) control.”
Motor learning. (2010, background): “Not only can people scale their overall stiffness (hence compliance), they can also shape the pattern of stiffness either by varying muscle activations or the posture of the arm [11].”