Visual flow influences gait transition speed and preferred walking speed

Mohler, Betty J.; Thompson, William B.; Creem-Regehr, Sarah H.; Pick, Herbert L.; Warren, William H.

doi:10.1007/s00221-007-0917-0

Cited by 254 publications

(152 citation statements)

References 33 publications

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“…The walk-run and run-walk transition distribution of ostriches exhibited gait-transition hysteresis, and such directional effects on the preferred gait-transition speed cannot be explained by steady-state CoT curves. A similar gait-transition hysteresis has been observed in humans (Thorstensson and Roberthson, 1987;Mohler et al, 2007), and human studies suggest that neural processing and fatigue factors probably influence gait-transition speeds (Mohler et al, 2007;Segers et al, 2007). Thus, while gait transitions of ostriches occur near the energetically optimum speeds (Fig.…”

Section: Gait-transition Speeds and Hysteresissupporting

confidence: 65%

Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Daley

Channon

Nolan

et al. 2016

Journal of Experimental Biology

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The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait-speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk-run and run-walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk-run and run-walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous labbased measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.

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Section: Gait-transition Speeds and Hysteresissupporting

confidence: 65%

Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Daley

Channon

Nolan

et al. 2016

Journal of Experimental Biology

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“…Previous work has shown that subjects adjust average walking speed after sustained changes in visually presented speed and in a direction opposite the change in perceived speed (De Smet et al 2009;Mohler et al 2007b). However, measures of average speed over a trial mask the amplitude and timing of the fast dynamics that produce this outcome as well as the slow process that eventually cancels it out.…”

Section: Discussionmentioning

confidence: 99%

“…For example, prior knowledge of the association between energy expenditure and walking speed would allow one to immediately predict the energetically optimum adjustments to speed based only on the currently sensed speed. The visual pathways are known to be partially responsible for sensing and making adjustments to walking speed: increasing visual flow rate relative to the actual walking speed elicits significantly slower average preferred speeds, and vice versa (De Smet et al 2009;Mohler et al 2007a;Prokop et al 1997). However, it is still unclear whether vision is used for the rapid gait adjustments that are indicative of indirect prediction, since these studies only used slow visual perturbations that cannot identify the dynamics of the processes involved.…”

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confidence: 99%

Fast visual prediction and slow optimization of preferred walking speed

O’Connor

Donelan

2012

Journal of Neurophysiology

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O'Connor SM, Donelan JM. Fast visual prediction and slow optimization of preferred walking speed. J Neurophysiol 107: 2549-2559, 2012. First published February 1, 2012 doi:10.1152/jn.00866.2011.-People prefer walking speeds that minimize energetic cost. This may be accomplished by directly sensing metabolic rate and adapting gait to minimize it, but only slowly due to the compounded effects of sensing delays and iterative convergence. Visual and other sensory information is available more rapidly and could help predict which gait changes reduce energetic cost, but only approximately because it relies on prior experience and an indirect means to achieve economy. We used virtual reality to manipulate visually presented speed while 10 healthy subjects freely walked on a self-paced treadmill to test whether the nervous system beneficially combines these two mechanisms. Rather than manipulating the speed of visual flow directly, we coupled it to the walking speed selected by the subject and then manipulated the ratio between these two speeds. We then quantified the dynamics of walking speed adjustments in response to perturbations of the visual speed. For step changes in visual speed, subjects responded with rapid speed adjustments (lasting Ͻ2 s) and in a direction opposite to the perturbation and consistent with returning the visually presented speed toward their preferred walking speed, when visual speed was suddenly twice (one-half) the walking speed, subjects decreased (increased) their speed. Subjects did not maintain the new speed but instead gradually returned toward the speed preferred before the perturbation (lasting Ͼ300 s). The timing and direction of these responses strongly indicate that a rapid predictive process informed by visual feedback helps select preferred speed, perhaps to complement a slower optimization process that seeks to minimize energetic cost. visual feedback; energetics; locomotion OF ALL THE POSSIBLE WAYS TO WALK, people prefer specific patterns. For example, rather than use our full range of possible walking speeds, we prefer a narrow speed range and will switch to a running gait if we have to move much faster

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“…Participants were asked to match their power output and pedaling cadence to the average values calculated from their first self-paced time trial. It was necessary to give participants voluntary control of power and cadence in this way so that, consistent with the methods used by Mohler, Thompson, Creem-Regehr, Pick and Warren (2007), the projected video footage was coupled in a multiplicative way to the cyclists' actual power output. The alternative-fixed optic flow video footage-would allow us to precisely fix power and cadence, but, owing to the video being uncoupled to actual cycling behavior creates a far less realistic virtual experience.…”

Section: Cycling Ergometrymentioning

confidence: 99%

Optic Flow Influences Perceived Exertion During Cycling

Chinnasamy

Micklewright

2012

Journal of Sport and Exercise Psychology

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Optic flow on the retina creates a perception of a person's movement relative to their surroundings. This study investigated the effect of optic flow on perceived exertion during cycling. Fifteen participants completed a 20-km reference cycling time trail in the fastest possible time followed by three randomly counterbalanced 20-km cycling trials. Optic flow, via projected video footage of a cycling course, either represented actual speed (TT NORM ) or was varied by -15% (TT SLOW ) and +15% (TT FAST ). During TT SLOW , power output and ratings of perceived exertion (RPE), measured every 4 km, were lower during TT SLOW compared with TT NORM and TT FAST . There were no differences in heart rate or cadence. This study is the first to show that different rates of optic flow influence perceived exertion during cycling, with slower optic flow being associated with lower RPE and higher power output.

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Visual flow influences gait transition speed and preferred walking speed

Cited by 254 publications

References 33 publications

Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Fast visual prediction and slow optimization of preferred walking speed

Optic Flow Influences Perceived Exertion During Cycling

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