Vision Modulates Corticospinal Suppression in a Functionally Specific Manner during Movement of the Opposite Limb

The effect of vision on the excitability of corticospinal projections to the flexor carpi radialis (FCR) and extensor carpi radialis (ECR) muscles of right human forearm was investigated before and during discrete movement of the opposite limb. An external force opposed the initial phase of the movement (wrist flexion) and assisted the reverse phase, so that recruitment of the wrist extensors was minimized. Three conditions were used as follows: viewing the inactive right limb (Vision), viewing the mirror image of the moving left limb (Mirror), and with vision of the right limb occluded (No Vision). Transcranial magnetic stimulation was delivered to the left motor cortex: before, at the onset of, or during the left limb movement to obtain motor evoked potentials (MEPs) in the muscles of the right forearm. At and following movement onset, MEPs obtained in the right FCR were smaller in the Vision condition than in the Mirror and No Vision conditions. A distinct pattern of variation was obtained for the ECR. In all conditions, MEPs in this muscle were elevated upon or following movement of the opposite limb. An additional analysis of ipsilateral silent periods indicated that interhemispheric inhibition plays a role in mediating these effects. Activity-dependent changes in corticospinal output to a resting limb during discrete actions of the opposite limb are thus directly contingent upon where one looks. Furthermore, the extent to which vision exerts an influence upon projections to specific muscles varies in accordance with the functional contribution of their homologs to the intended action.

Human pose tracking in low dimensional space enhanced by limb correction

This paper proposes a two-level 3D human pose tracking method for a specific action captured by several cameras. The generation of pose estimates relies on fitting a 3D articulated model on a Visual Hull generated from the input images. First, an initial pose estimate is constrained by a low dimensional manifold learnt by Temporal Laplacian Eigenmaps. Then, an improved global pose is calculated by refining individual limb poses. The validation of our method uses a public standard dataset and demonstrates its accurate and computational efficiency. © 2011 IEEE.