Natural Locomotion Free Download [HOT]

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The goal of this paper is to measure eye, body, and head movements during natural locomotion and to use this data to investigate the resulting optic flow patterns. We first calculated the flow patterns relative to the head, as this reflects the way that the movement of the body during gait impacts instantaneous heading direction by showing an eye-movement-free representation of optic flow. Then, we combine these head-centered flowfields with measured eye position to estimate the retinal optic flow experienced during natural locomotion. By characterizing the optic flow stimulus experienced during natural locomotion, we may gain a greater insight into the ways that the nervous system could exploit these signals for locomotor control.

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Stabilization of gaze during fixation nulls visual motion at the fovea, so the basic structure of retinal optic flow will always consist of outflowing motion centered on the point of fixation. The retinal motion results from the translation and rotation of the eye in space, carried by the body and the walker holds gaze on a point on the ground during forward movement. We found several features of the retinal flow patterns that provide powerful cues for the visual control of locomotion, which we describe below.

In experiments that use a stationary observer and simulate direction on a computer monitor, the strong sense of illusory motion (vection) and accurate estimates of simulated heading indicate that humans are highly sensitive to full field optic flow (e.g. [10, 73]). However, it does not necessarily mean that subjects use this information to control direction of the body when heading towards a distant goal. The complex, phasic pattern of acceleration shown here derives from the basic biomechanics of locomotion [50]. In the absence of direct measurements of flow during locomotion, the magnitude of the effect of gait has not been obvious. Thus it may have been incorrectly assumed that the overall structure of optic flow during locomotion would be dominated by the effects of forward motion. Such a forward-motion-dominated might be derived from temporal integration of eye-movement-corrected, head-centered optic flow, but given the large and rapid variation in the head velocity shown here it is unclear if simple temporal integration would be sufficient for accurate heading estimates.

The act of steering towards a goal does not necessarily require the use of optic flow. [86] proposed that the perceived location (visual direction) of a target with respect to the body is used to guide locomotion, rendering optic flow unnecessary. Perhaps the strongest evidence for the role of optic flow in control of steering towards a goal is the demonstration by [87] who pitted visual direction against the focus of expansion in a virtual environment, where walkers generate the flow patterns typical of natural locomotion. They found that although visual direction was used to control walking paths when environments lacked visual structure (and thereby lacked a salient optic flow signal), optic flow had an increasing effect on paths as environments became more structured. The authors interpreted this result to mean that walkers use a combination of visual direction and optic flow to steer to a goal when the visual environment contains sufficient visual structure. This is puzzling in the context of our findings, since the [87] experiment used a fully ambulatory virtual environment, so the head-centered optic flow experienced those subjects would have had the same instabilities described here. How then can we reconcile these results?

While many methods exist to compute instantaneous heading from the retinal flow field, a consideration of these patterns relative to the gaze point through the gait cycle provides a different context for the way the retinal flow information is used to control real-world, natural locomotion.

We created a geometric simulation to provide a more nuanced picture of the way that the movement of the body shapes the visual motion experienced during natural locomotion. To estimate the flow experienced during various types of movements, a simulated eye model was generated using the following procedure. Most of the geometric calculations used in this model rely heavily on the Geom3D toolbox on Mathworks.com [98]

We have shown in other work that the location of gaze is tightly linked to the terrain complexity, and saccade timing linked to phase of the gait cycle. Therefore, we chose to examine the periods of stable gaze, where there is consensus that humans are collecting image motion necessary for controlling locomotion. The retinal image motion caused by saccades is of course of interest and must be dealt with by the visual system in ways that are not well understood, e.g. [55]. For completeness, we calculated the additional image motion on the ground plane engendered by a saccade (195 degrees per second, up and to the left) and this is shown in Fig 10.

Walking through an environment generates retinal motion, which humans rely on to perform a variety of visual tasks. Retinal motion patterns are determined by an interconnected set of factors, including gaze location, gaze stabilization, the structure of the environment, and the walker's goals. The characteristics of these motion signals have important consequences for neural organization and behavior. However, to date, there are no empirical in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. Here, we collect measurements of the eyes, the body, and the 3D environment during locomotion. We describe properties of the resulting retinal motion patterns. We explain how these patterns are shaped by gaze location in the world, as well as by behavior, and how they may provide a template for the way motion sensitivity and receptive field properties vary across the visual field.

The articles in this volume were invited and fully refereed. They provide a representative if necessarily incomplete account of the field of natural locomotion during a period of rapid growth and expansion. The papers presented at the workshop, and the contributions to the present volume, can be roughly divided into those pertaining to swimming on the scale of marine organisms, swimming of microorganisms at low Reynolds numbers, animal flight, and sliding and other related examples of locomotion.

Back to top Keywords Insect flightNatural locomotionfluid dynamics in naturemodel of fish schoolingnewtonian dynamics of insect flightfluid- and aerodynamics Back to top Editors and Affiliations Courant Institute of Math Sciences, New York University, New York City, USA Stephen Childress

Despite many elegant theoretical analyses of the way that observer motion generates retinal flow patterns, a detailed understanding has been limited by the difficulties in recording the visual input during locomotion in natural environments. In this article, we measure eye and body movements during locomotion in a variety of natural terrains and explore how they shape the properties of the retinal input. A number of studies have examined motion patterns generated by cameras moving through natural environments (Betsch et al., 2005; Zanker and Zeil, 2005), but these data do not accurately reflect the patterns incident on the human retinae because the movement of the cameras does not mimic the movements of the head, nor does it take into account the location of gaze. In natural locomotion, walkers gaze at different locations depending on the complexity of the terrain and the consequent need to find stable footholds (Matthis et al., 2018). Thus, task goals indirectly affect the motion input. In addition, natural locomotion is not linear. Instead, the head moves through a complex trajectory in space during the gait cycle, while the point of gaze remains stable in the environment, and this imparts a complex pattern of rotation and expansion on the retinal flow as recently described by Matthis et al., 2021. Retinal motion is generated by the compensatory rotations of the eye in space while the body moves forward during a step, and gaze is held at a fixed location in space. To characterize the properties of this motion and how it depends on gaze behavior, we simultaneously recorded gaze and image data while subjects walked in a variety of different natural terrains. In addition, to fully characterize the retinal motion we reconstructed a 3D representation of the terrain. This links the eye and body movements to the particular terrain and consequently allows calculation of the motion patterns on the retinae.

(a) Schematic of a saccade and subsequent gaze stabilization during locomotion when looking at the nearby ground. In the top left, the walker makes a saccade to an object further along the path. In the middle panel, the walker fixates (holds gaze) at this location for a time. The right panel shows the gaze angle becoming more normal to the ground plane during stabilization. (b) Excerpt of vertical gaze angle relative to gravity during a period of saccades and subsequent stabilization. As participants move forward while looking at the nearby ground, they make sequences of saccades (indicated by the gaps in the trace) to new locations, followed by fixations where gaze is held stable at a location in the world while the body moves forward along the direction of travel (indicated by the lower velocity green traces). The higher velocity saccades were detected as described in the text based on both horizontal and vertical velocity and acceleration. These are followed by slower counter-rotations of the eye in the orbit in order to maintain gaze at a fixed location in the scene (the gray time slices). 006ab0faaa