Human locomotion over obstacles reveals real-time prediction of energy expenditure for optimized decision-making

Despite decades of evidence revealing a multitude of ways in which animals are adapted to minimize the energy cost of locomotion, little is known about how energy expenditure shapes adaptive gait over complex terrain. Here, we show that the principle of energy optimality in human locomotion can be generalized to complex task-level locomotor behaviours requiring advance decision-making and anticipatory control. Participants completed a forced-choice locomotor task requiring them to choose between discrete multi-step obstacle negotiation strategies to cross a 'hole' in the ground. By modelling and analysing mechanical energy cost of transport for preferred and non-preferred manoeuvres over a wide range of obstacle dimensions, we showed that strategy selection was predicted by relative energy cost integrated across the complete multi-step task. Vision-based remote sensing was sufficient to select the strategy associated with the lowest prospective energy cost in advance of obstacle encounter, demonstrating the capacity for energetic optimization of locomotor behaviour in the absence of online proprioceptive or chemosensory feedback mechanisms. We highlight the integrative hierarchic optimizations that are required to facilitate energetically efficient locomotion over complex terrain and propose a new behavioural level linking mechanics, remote sensing and cognition that can be leveraged to explore locomotor control and decision-making.

Automated summary

This study demonstrates that the principle of energy optimality in human locomotion can be applied to complex task-level locomotor behaviors. The selection of locomotor strategies is predicted by the relative energy cost integrated across the complete multi-step task. Vision-based remote sensing alone is sufficient to predict the strategy with the lowest prospective energy cost in advance of obstacle encounter. This research highlights the integrative optimizations necessary for energetically efficient locomotion and proposes a new behavioral level that can be used to explore locomotor control and decision-making.