The separate neural control of hand movements and contact forces

To manipulate an object, we must simultaneously control the contact forces exerted on the object and the movements of our hand. Two alternative views for manipulation have been proposed: one in which motions and contact forces are represented and controlled by separate neural processes, and one in which motions and forces are controlled jointly, by a single process. To evaluate these alternatives, we designed three tasks in which subjects maintained a specified contact force while their hand was moved by a robotic manipulandum. The prescribed contact force and hand motions were selected in each task to induce the subject to attain one of three goals: (1) exerting a regulated contact force, (2) tracking the motion of the manipulandum, and (3) attaining both force and motion goals concurrently. By comparing subjects' performances in these three tasks, we found that behavior was captured by the summed actions of two independent control systems: one applying the desired force, and the other guiding the hand along the predicted path of the manipulandum. Furthermore, the application of transcranial magnetic stimulation impulses to the posterior parietal cortex selectively disrupted the control of motion but did not affect the regulation of static contact force. Together, these findings are consistent with the view that manipulation of objects is performed by independent brain control of hand motions and interaction forces

Use of Self-Selected Postures to Regulate Multi-Joint Stiffness During Unconstrained Tasks

The human motor system is highly redundant, having more kinematic degrees of freedom than necessary to complete a given task. Understanding how kinematic redundancies are utilized in different tasks remains a fundamental question in motor control. One possibility is that they can be used to tune the mechanical properties of a limb to the specific requirements of a task. For example, many tasks such as tool usage compromise arm stability along specific directions. These tasks only can be completed if the nervous system adapts the mechanical properties of the arm such that the arm, coupled to the tool, remains stable. The purpose of this study was to determine if posture selection is a critical component of endpoint stiffness regulation during unconstrained tasks.Three-dimensional (3D) estimates of endpoint stiffness were used to quantify limb mechanics. Most previous studies examining endpoint stiffness adaptation were completed in 2D using constrained postures to maintain a non-redundant mapping between joint angles and hand location. Our hypothesis was that during unconstrained conditions, subjects would select arm postures that matched endpoint stiffness to the functional requirements of the task. The hypothesis was tested during endpoint tracking tasks in which subjects interacted with unstable haptic environments, simulated using a 3D robotic manipulator. We found that arm posture had a significant effect on endpoint tracking accuracy and that subjects selected postures that improved tracking performance. For environments in which arm posture had a large effect on tracking accuracy, the self-selected postures oriented the direction of maximal endpoint stiffness towards the direction of the unstable haptic environment.These results demonstrate how changes in arm posture can have a dramatic effect on task performance and suggest that postural selection is a fundamental mechanism by which kinematic redundancies can be exploited to regulate arm stiffness in unconstrained tasks

Endpoint stiffness ellipsoids from a typical subject.

<p>Two views are shown for the self-selected postures used for each haptic environment. At each posture, stiffness was estimated as the subject applied a +10 N force along the direction of the haptic instability.</p

Self-selected postures during interactions with each of the haptic environments.

<p>Postures were chosen during interactions with unstable environments aligned to the X (A), Y (B) and Z (C) axes. Each filled circle corresponds to the posture chosen by a single subject. Dashed lines correspond to 90% confidence interval ellipses, computed from the covariance between shoulder angle and hand position <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005411#pone.0005411-Press1" target="_blank">[45]</a>. The characters are placed at locations along horizontal and vertical axes corresponding to the posture that they depict.</p

Experimental setup for tracking task.

<p>Subjects stood upright and used the arm to interact with unstable haptic environments oriented along the X (A), Y (B) or Z (C) measurement axes. During target tracking, movements were constrained to lie along these axes by 50 kN/m virtual walls.</p

Nonparametric estimates of endpoint impedance for a single experimental condition.

<p>The subject's posture placed the hand at 190 mm in front of the sternum and had the shoulder abducted to 14°. (A) Nonparametric transfer functions (gray lines) and the corresponding second-order fits (black lines). (B) Multiple coherence functions for forces along each of the three measurement axes. Horizontal dashed lines (1.0) correspond to perfect coherence.</p

Refined estimate of endpoint displacement.

<p>An instrumental variable (IV) technique was used to increase the accuracy of the measured endpoint displacement. The figure shows typical data measured along the X axis. The displacement measured by the robot sensors is shown by the black dashed line; it differs from the true displacement due to compliance in the robot transmission. A noisy, but more accurate estimate was obtained using optical tracking (thick gray line). The combined estimate, obtained using instrumental variables, is shown by the solid black line.</p

Haptic environments used during endpoint tracking.

*<p>denotes that subject participated in endpoint stiffness experiment.</p