Locomotor Coordination, Visual Perception and Head Stability during Running

Abstract

Perception and action are coupled such that information from the perceptual system is related to the dynamics of action in order to regulate behavior adaptively. Using running as a model of a cyclic behavior, this coupling involves a continuous, cyclic relationship between the runner's perception of the environment and the necessary adjustments of the body that ultimately result in a stable pattern of behavior. The purpose of this paper is to illustrate how individuals relate visual perception to rhythmic locomotor coordination patterns in conditions during which foot-ground collisions and visual task demands are altered. We review the findings of studies conducted to illustrate how humans change their behavior to maintain head stability during running with and without various degrees of visual challenge from the environment. Finally, we show that the human body adapts specific segment/joint configuration and coordination patterns to maintain head stability, both in the lower extremity and upper body segments, together with an increase in coordinative variability. These results indicate that in human locomotion, under higher speed (running) and visual task demands, systematic adaptations occur in the rhythmic coupling between the perceptual and movement systems.

Keywords: action; coordination; impact shock; perception; shock attenuation.

Figure 1

A schematic illustrating Perception–Action Coupling.

Figure 2

Mean (±SD) of impact peak of tibia and resulting peak of the head during the foot–ground collisions at the preferred stride frequency (PSF) and plus and minus percentages of the PSF (adapted from data in Hamill et al. [20]).

Figure 3

Tibial acceleration profile in the frequency domain.

Figure 4

Experimental setup as in Busa et al. [43]. Kinematic data were collected along with tibial and head accelerations. The participants ran at their preferred speed and percentages of their preferred stride frequency. A screen was positioned approximately 2.5 m away from the treadmill center. The dotted line from the participant’s head in (a) and (c) indicates an imaginary line of a head gaze vector created from the motion capture data of the head. In the visual feedback condition (b-right), a dot indicating the intersection point of head gaze vector on the screen was displayed while running. In (b) the dotted line represents the trajectory of the head gaze point on the screen, this was not displayed during the testing. During the visual feedback condition (b), a square box (inset light box) was displayed to indicate the boundary area for the feedback. This box was created by subtending an angle 21° horizontally and vertically form the center of the treadmill centered at a height of 1.7 m above the treadmill belt.

Figure 5

A schematic of the square boxes representing the visual angle areas in which the head projection vector had to be maintained. The angles subtended by the box horizontally and vertically were varied in each condition from 21° to 3° of visual angle with 3 degree decrements. An example of one participant’s head movement is illustrated (note that the participant did not see the trace of the path; they only saw the movement of the dot from the projection vector). Adapted from Lim et al. [46].

Figure 6

Mean percent changes for key dependent measures showing the significant effect of visual condition from VA-21. Positive numbers indicate increased value in percentage from VA-21, and vice versa (adapted from Lim et al. [46]).

Figure 7

Mean percent change for the coordination patterns in lower extremity joint couples in the sagittal plane, hip–knee (a) and knee–ankle (b), and upper extremity segment couples in transverse plane, trunk-head (c), showing the significant effect of visual condition from VA-21. Main effect of visual condition (* p < 0.05; ** p < 0.01). Positive numbers indicate increased value in percentage from VA-21, and vice versa (Lim et al. [59]).

Figure 8

Coordination variability during stance in the head–trunk (a) and lower extremity (b and c) segment/joint couplings as a function of visual condition (left) and F (SPM{F}) values at each normalized time point (right). A horizontal dotted line in the right panel indicates threshold level in SPM statistics (Lim et al. [59]). Note. SPM indicates a statistical parameter mapping, providing a statistical solution for the objective classical hypothesis testing for differences between biomechanical time-series datasets (Pataky et al. [60]).