Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. Both models had good prediction accuracy despite using less input variables than traditional models and using more challenging prediction conditions by enforcing rapid speed changes during cycling. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

Why animals can outrun robots.

Animals are much better at running than robots. The difference in performance arises in the important dimensions of agility, range, and robustness. To understand the underlying causes for this performance gap, we compare natural and artificial technologies in the five subsystems critical for running: power, frame, actuation, sensing, and control. With few exceptions, engineering technologies meet or exceed the performance of their biological counterparts. We conclude that biology's advantage over engineering arises from better integration of subsystems, and we identify four fundamental obstacles that roboticists must overcome. Toward this goal, we highlight promising research directions that have outsized potential to help future running robots achieve animal-level performance.

Causal modelling demonstrates metabolic power is largely affected by gait kinematics and motor control in children with cerebral palsy.

Metabolic power (net energy consumed while walking per unit time) is, on average, two-to-three times greater in children with cerebral palsy (CP) than their typically developing peers, contributing to greater physical fatigue, lower levels of physical activity and greater risk of cardiovascular disease. The goal of this study was to identify the causal effects of clinical factors that may contribute to high metabolic power demand in children with CP. We included children who 1) visited Gillette Children's Specialty Healthcare for a quantitative gait assessment after the year 2000, 2) were formally diagnosed with CP, 3) were classified as level I-III under the Gross Motor Function Classification System and 4) were 18 years old or younger. We created a structural causal model that specified the assumed relationships of a child's gait pattern (i.e., gait deviation index, GDI) and common impairments (i.e., dynamic and selective motor control, strength, and spasticity) with metabolic power. We estimated causal effects using Bayesian additive regression trees, adjusting for factors identified by the causal model. There were 2157 children who met our criteria. We found that a child's gait pattern, as summarized by the GDI, affected metabolic power approximately twice as much as the next largest contributor. Selective motor control, dynamic motor control, and spasticity had the next largest effects. Among the factors we considered, strength had the smallest effect on metabolic power. Our results suggest that children with CP may benefit more from treatments that improve their gait pattern and motor control than treatments that improve spasticity or strength.

General variability leads to specific adaptation toward optimal movement policies.

Our nervous systems can learn optimal control policies in response to changes to our bodies, tasks, and movement contexts. For example, humans can learn to adapt their control policy in walking contexts where the energy-optimal policy is shifted along variables such as step frequency or step width. However, it is unclear how the nervous system determines which ways to adapt its control policy. Here, we asked how human participants explore through variations in their control policy to identify more optimal policies in new contexts. We created new contexts using exoskeletons that apply assistive torques to each ankle at each walking step. We analyzed four variables that spanned the levels of the whole movement, the joint, and the muscle: step frequency, ankle angle range, total soleus activity, and total medial gastrocnemius activity. We found that, across all of these analyzed variables, variability increased upon initial exposure to new contexts and then decreased with experience. This led to adaptive changes in the magnitude of specific variables, and these changes were correlated with reduced energetic cost. The timescales by which adaptive changes progressed and variability decreased were faster for some variables than others, suggesting a reduced search space within which the nervous system continues to optimize its policy. These collective findings support the principle that exploration through general variability leads to specific adaptation toward optimal movement policies.

Characterizing the performance of human leg external force control.

Our legs act as our primary contact with the surrounding environment, generating external forces that enable agile motion. To be agile, the nervous system has to control both the magnitude of the force that the feet apply to the ground and the point of application of this force. The purpose of this study was to characterize the performance of the healthy human neuromechanical system in controlling the force-magnitude and position of an externally applied force. To accomplish this, we built an apparatus that immobilized participants but allowed them to exert variable but controlled external forces with a single leg onto a ground embedded force plate. We provided real-time visual feedback of either the leg force-magnitude or force-position that participants were exerting against the force platform and instructed participants to best match their real-time signal to prescribed target step functions. We tested target step functions of a range of sizes and quantified the responsiveness and accuracy of the control. For the control of force-magnitude and for intermediate step sizes of 0.45 bodyweights, we found a bandwidth of 1.8 ± 0.5 Hz, a steady-state error of 2.6 ± 0.9%, and a steady-state variability of 2.7 ± 0.9%. We found similar control performance in terms of responsiveness and accuracy across step sizes and between force-magnitude and position control. Increases in responsiveness correlated with reductions in other measures of control performance, such as a greater magnitude of overshooting. We modelled the observed control performance and found that a second-order model was a good predictor of external leg force control. We discuss how benchmarking force control performance in young healthy humans aids in understanding differences in agility between humans, between humans and other animals, and between humans and engineered systems.

A remote laboratory course on experimental human physiology using wearable technology.

To help educators deliver their physiology laboratory courses remotely, we developed an inexpensive, customizable hardware kit along with freely available teaching resources. We based the course design on four principles that should allow students to conduct insightful experiments on different physiological systems. First, the experimental setup should not be constrained to laboratory environments. Second, students should be able to take this course without prior coding and electronics experience. Third, the hardware kit should be relatively inexpensive, and all other resources should be freely available. Fourth, all resources should be customizable for educators. The hardware kit consists of commercially available electronic components, with a microcontroller as its hub (Arduino friendly). All measurement systems can be assembled without soldering. The hardware kit is cost-effective (approximately the cost of a textbook) and can be customized depending upon instructional needs. All software is freely available, and we share all necessary codes in open-access online repositories for simple use and customizability. All lab manuals and additional video tutorials are also freely available online and customizable. In our particular course, we have weekly asynchronous physiology lectures and one synchronous laboratory session, where students can get help with their equipment. In this article, we only focus on the novel and open-source laboratory part of the course. The laboratory includes four units [data acquisition, ECG, electromyography (EMG), activity classification] and one final project. It is our intent that these resources will allow other educators to rapidly implement their own remote physiology laboratories or to extend our work into other pedagogical applications of wearable technology.

Energy optimization during walking involves implicit processing.

Gait adaptations, in response to novel environments, devices or changes to the body, can be driven by the continuous optimization of energy expenditure. However, whether energy optimization involves implicit processing (occurring automatically and with minimal cognitive attention), explicit processing (occurring consciously with an attention-demanding strategy) or both in combination remains unclear. Here, we used a dual-task paradigm to probe the contributions of implicit and explicit processes in energy optimization during walking. To create our primary energy optimization task, we used lower-limb exoskeletons to shift people's energetically optimal step frequency to frequencies lower than normally preferred. Our secondary task, designed to draw explicit attention from the optimization task, was an auditory tone discrimination task. We found that adding this secondary task did not prevent energy optimization during walking; participants in our dual-task experiment adapted their step frequency toward the optima by an amount and at a rate similar to participants in our previous single-task experiment. We also found that performance on the tone discrimination task did not worsen when participants were adapting toward energy optima; accuracy scores and reaction times remained unchanged when the exoskeleton altered the energy optimal gaits. Survey responses suggest that dual-task participants were largely unaware of the changes they made to their gait during adaptation, whereas single-task participants were more aware of their gait changes yet did not leverage this explicit awareness to improve gait adaptation. Collectively, our results suggest that energy optimization involves implicit processing, allowing attentional resources to be directed toward other cognitive and motor objectives during walking.

Increasing the gradient of energetic cost does not initiate adaptation in human walking.

When in a new situation, the nervous system may benefit from adapting its control policy. In determining whether or not to initiate this adaptation, the nervous system may rely on some features of the new situation. Here, we tested whether one such feature is salient cost savings. We changed cost saliency by manipulating the gradient of participants' energetic cost landscape during walking. We hypothesized that steeper gradients would cause participants to spontaneously adapt their step frequency to lower costs. To manipulate the gradient, a mechatronic system applied controlled fore-aft forces to the waist of participants as a function of their step frequency as they walked on a treadmill. These forces increased the energetic cost of walking at high step frequencies and reduced it at low step frequencies. We successfully created three cost landscapes of increasing gradients, where the natural variability in participants' step frequency provided cost changes of 3.6% (shallow), 7.2% (intermediate), and 10.2% (steep). Participants did not spontaneously initiate adaptation in response to any of the gradients. Using metronome-guided walking-a previously established protocol for eliciting initiation of adaptation-participants next experienced a step frequency with a lower cost. Participants then adapted by -1.41 ± 0.81 (P = 0.007) normalized units away from their originally preferred step frequency obtaining cost savings of 4.80% ± 3.12%. That participants would adapt under some conditions, but not in response to steeper cost gradients, suggests that the nervous system does not solely rely on the gradient of energetic cost to initiate adaptation in novel situations.NEW & NOTEWORTHY People can adapt to novel conditions but often require cues to initiate the adaptation. Using a mechatronic system to reshape energetic cost gradients during treadmill walking, we tested whether the nervous system can use information present in the cost gradient to spontaneously initiate adaptation. We found that our participants did not spontaneously initiate adaptation even in the steepest gradient. The nervous system does not rely solely on the cost gradient when initiating adaptation.

Savings in sensorimotor learning during balance-challenged walking but not reaching.

Safe and successful motor performance relies on the ability to adapt to physiological and environmental change and retain what is learned. An open question is what factors maximize this retention? One overlooked factor is the degree to which balance is challenged during learning. We propose that the greater need for control and/or perceived threat of falling or injury associated with balance-challenging tasks increases the value assigned to maintaining a learned visuomotor mapping (i.e., the new relationship between visual input and motor output). And we propose that a greater-valued mapping is a more retainable mapping, as it serves to benefit future motor performance. Thus, we tested the hypothesis that challenging balance enhances motor memory, reflected by greater recall and faster relearning (i.e., savings). Four groups of participants adapted to a novel visuomotor mapping induced by prism lenses while performing a reaching or walking task, with and without an additional balance challenge. We found that challenging balance did not disrupt visuomotor adaptation during reaching or walking. We then probed recall and savings by having participants repeat the adaptation protocol 1 wk later. For reaching, we found evidence of initial recall, though neither group demonstrated savings upon reexposure to the prisms. In contrast, both walking groups demonstrated significant initial recall and savings. In addition, we found that challenging balance significantly enhanced savings during walking. Taken together, our results demonstrate the robustness of motor memories formed during walking and highlight the potential influence of balance control on sensorimotor learning.NEW & NOTEWORTHY Most everyday tasks challenge our balance. Yet, this aspect of daily motor behavior is often overlooked in adaptation paradigms. Here, we show that challenging balance does not impair sensorimotor adaptation during precision reaching and walking tasks. Furthermore, we show that challenging balance enhances savings of a learned visuomotor mapping during walking. These results provide evidence for the potential performance benefits associated with learning during unconstrained, naturalistic behaviors.

Using asymmetry to your advantage: learning to acquire and accept external assistance during prolonged split-belt walking.

People can learn to exploit external assistance during walking to reduce energetic cost. For example, walking on a split-belt treadmill affords the opportunity for people to redistribute the mechanical work performed by the legs to gain assistance from the difference in belts' speed and reduce energetic cost. Though we know what people should do to acquire this assistance, this strategy is not observed during typical adaptation studies. We hypothesized that extending the time allotted for adaptation would result in participants adopting asymmetric step lengths to increase the assistance they can acquire from the treadmill. Here, participants walked on a split-belt treadmill for 45 min while we measured spatiotemporal gait variables, metabolic cost, and mechanical work. We show that when people are given sufficient time to adapt, they naturally learn to step further forward on the fast belt, acquire positive mechanical work from the treadmill, and reduce the positive work performed by the legs. We also show that spatiotemporal adaptation and energy optimization operate over different timescales: people continue to reduce energetic cost even after spatiotemporal changes have plateaued. Our findings support the idea that walking with symmetric step lengths, which is traditionally thought of as the endpoint of adaptation, is only a point in the process by which people learn to take advantage of the assistance provided by the treadmill. These results provide further evidence that reducing energetic cost is central in shaping adaptive locomotion, but this process occurs over more extended timescales than those used in typical studies.NEW & NOTEWORTHY Split-belt treadmill adaptation can be seen as a process where people learn to acquire positive work from the treadmill to reduce energetic cost. Though we know what people should do to reduce energetic cost, this strategy is not observed during adaptation studies. We extended the duration of adaptation and show that people continuously adapt their gait to acquire positive work from the treadmill to reduce energetic cost. This process requires longer exposure than traditionally allotted.

Energy consumption does not change after selective dorsal rhizotomy in children with spastic cerebral palsy.

AIM: To determine whether energy consumption changes after selective dorsal rhizotomy (SDR) among children with cerebral palsy (CP). METHOD: We retrospectively evaluated net nondimensional energy consumption during walking among 101 children with bilateral spastic CP who underwent SDR (59 males, 42 females; median age [5th centile, 95th centile] 5y 8mo [4y 2mo, 9y 4mo]) compared to a control group of children with CP who did not undergo SDR. The control group was matched by baseline age, spasticity, and energy consumption (56 males, 45 females; median age [5th centile, 95th centile] 5y 8mo [4y 1mo, 9y 6mo]). Outcomes were compared at baseline and follow-up (SDR: mean [SD] 1y 7mo [6mo], control: 1y 8mo [8mo]). RESULTS: The SDR group had significantly greater decreases in spasticity compared to matched controls (-42% SDR vs -20% control, p<0.001). While both groups had a modest reduction in energy consumption between visits (-12% SDR, -7% control), there was no difference in change in energy consumption (p=0.11) or walking speed (p=0.56) between groups. INTERPRETATION: The SDR group did not exhibit greater reductions in energy consumption compared to controls. The SDR group had significantly greater spasticity reduction, suggesting that spasticity had minimal impact on energy consumption during walking in CP. These results support prior findings that spasticity and energy consumption decrease with age in CP. Identifying matched control groups is critical for outcomes research involving children with CP to account for developmental changes.

Challenging balance during sensorimotor adaptation increases generalization.

From reaching to walking, real-life experience suggests that people can generalize between motor behaviors. One possible explanation for this generalization is that real-life behaviors often challenge our balance. We propose that the exacerbated body motions associated with balance-challenged whole body movements increase the value to the nervous system of using a comprehensive internal model to control the task. Because it is less customized to a specific task, a more comprehensive model is also a more generalizable model. Here we tested the hypothesis that challenging balance during adaptation would increase generalization of a newly learned internal model. We encouraged participants to learn a new internal model using prism lenses that created a new visuomotor mapping. Four groups of participants adapted to prisms while performing either a standing-based reaching or precision walking task, with or without a manipulation that challenged balance. To assess generalization after the adaptation phase, participants performed a single trial of each of the other groups' tasks without prisms. We found that both the reaching and walking balance-challenged groups showed significantly greater generalization to the equivalent, nonadapted task than the balance-unchallenged groups. Additionally, we found some evidence that all groups generalized across tasks, for example, from walking to reaching and vice versa, regardless of balance manipulation. Overall, our results demonstrate that challenging balance increases the degree to which a newly learned internal model generalizes to untrained movements.NEW & NOTEWORTHY Real-life experience indicates that people can generalize between motor behaviors. Here we show that challenging balance during the learning of a new internal model increases the degree of generalization to untrained movements for both reaching and walking tasks. These results suggest that the effects of challenging balance are not specific to the task but instead apply to motor learning more broadly.

Scaling of inertial delays in terrestrial mammals.

As part of its response to a perturbation, an animal often needs to reposition its body. Inertia acts to oppose the corrective motion, delaying the completion of the movement-we refer to this elapsed time as inertial delay. As animal size increases, muscle moment arms also increase, but muscles are proportionally weaker, and limb inertia is proportionally larger. Consequently, the scaling of inertial delays is complex. Our intent is to determine how quickly different sized animals can produce corrective movements when their muscles act at their force capacity, relative to the time within which those movements need to be performed. Here, we quantify inertial delay using two biomechanical models representing common scenarios in animal locomotion: a distributed mass pendulum approximating swing limb repositioning (swing task), and an inverted pendulum approximating whole body posture recovery (posture task). We parameterized the anatomical, muscular, and inertial properties of these models using literature scaling relationships, then determined inertial delay for each task across a large range of movement magnitudes and the full range of terrestrial mammal sizes. We found that inertial delays scaled with an average of M0.28 in the swing task and M0.35 in the posture task across movement magnitudes-larger animals require more absolute time to perform the same movement as small animals. The time available to complete a movement also increases with animal size, but less steeply. Consequently, inertial delays comprise a greater fraction of swing duration and other characteristic movement times in larger animals. We also compared inertial delays to the other component delays within the stimulus-response pathway. As movement magnitude increased, inertial delays exceeded these sensorimotor delays, and this occurred for smaller movements in larger animals. Inertial delays appear to be a challenge for motor control, particularly for bigger movements in larger animals.

How humans initiate energy optimization and converge on their optimal gaits.

A central principle in motor control is that the coordination strategies learned by our nervous system are often optimal. Here, we combined human experiments with computational reinforcement learning models to study how the nervous system navigates possible movements to arrive at an optimal coordination. Our experiments used robotic exoskeletons to reshape the relationship between how participants walk and how much energy they consume. We found that while some participants used their relatively high natural gait variability to explore the new energetic landscape and spontaneously initiate energy optimization, most participants preferred to exploit their originally preferred, but now suboptimal, gait. We could nevertheless reliably initiate optimization in these exploiters by providing them with the experience of lower cost gaits, suggesting that the nervous system benefits from cues about the relevant dimensions along which to re-optimize its coordination. Once optimization was initiated, we found that the nervous system employed a local search process to converge on the new optimum gait over tens of seconds. Once optimization was completed, the nervous system learned to predict this new optimal gait and rapidly returned to it within a few steps if perturbed away. We then used our data to develop reinforcement learning models that can predict experimental behaviours, and applied these models to inductively reason about how the nervous system optimizes coordination. We conclude that the nervous system optimizes for energy using a prediction of the optimal gait, and then refines this prediction with the cost of each new walking step.

Energy optimization is a major objective in the real-time control of step width in human walking.

People prefer to move in energetically optimal ways during walking. We recently found that this preference arises not just through evolution and development, but that the nervous system will continuously optimize step frequency in response to new energetic cost landscapes. Here we tested whether energy optimization is also a major objective in the nervous system's real-time control of step width using a device that can reshape the relationship between step width and energetic cost, shifting people's energy optimal step width. We accomplished this by changing the walking incline to apply an energetic penalty as a function of step width. We found that people didn't spontaneously initiate energy optimization, but instead required experience with a lower energetic cost step width. After initiating optimization, people adapted, on average, 3.5 standard deviations of their natural step width variability towards the new energy optimal width. Within hundreds of steps, they updated this as their new preferred width and rapidly returned to it when perturbed away. This new preferred width reduced energetic cost by roughly 14%, however, it was slightly narrower than the energetically optimal width, possibly due to non-energy objectives that may contribute to the nervous system's control of step width. Collectively, these findings suggest that the nervous systems of able-bodied people can continuously optimize energetic cost to determine preferred step width.

Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split-belt treadmill walking.

KEY POINTS: The neuromotor system generates flexible motor patterns that can adapt to changes in our bodies or environment and also take advantage of assistance provided by the environment. We ask how energy minimization influences adaptive learning during human locomotion to improve economy when walking on a split-belt treadmill. We use a model-based approach to predict how people should adjust their walking pattern to take advantage of the assistance provided by the treadmill, and we validate these predictions empirically. We show that adaptation to a split-belt treadmill can be explained as a process by which people reduce step length asymmetry to take advantage of the work performed by the treadmill to reduce metabolic cost. Our results also have implications for the evaluation of devices designed to reduce effort during walking, as locomotor adaptation may serve as a model approach to understand how people learn to take advantage of external assistance. ABSTRACT: In everyday tasks such as walking and running, we often exploit the work performed by external sources to reduce effort. Recent research has focused on designing assistive devices capable of performing mechanical work to reduce the work performed by muscles and improve walking function. The success of these devices relies on the user learning to take advantage of this external assistance. Although adaptation is central to this process, the study of adaptation is often done using approaches that seem to have little in common with the use of external assistance. We show in 16 young, healthy participants that a common approach for studying adaptation, split-belt treadmill walking, can be understood from a perspective in which people learn to take advantage of mechanical work performed by the treadmill. Initially, during split-belt walking, people step further forward on the slow belt than the fast belt which we measure as a negative step length asymmetry, but this asymmetry is reduced with practice. We demonstrate that reductions in asymmetry allow people to extract positive work from the treadmill, reduce the positive work performed by the legs, and reduce metabolic cost. We also show that walking with positive step length asymmetries, defined by longer steps on the fast belt, minimizes metabolic cost, and people choose this pattern after guided experience of a wide range of asymmetries. Our results suggest that split-belt adaptation can be interpreted as a process by which people learn to take advantage of mechanical work performed by an external device to improve economy.

A Mechatronic System for Studying Energy Optimization During Walking.

A general principle of human movement is that our nervous system is able to learn optimal coordination strategies. However, how our nervous system performs this optimization is not well understood. Here we design, build, and test a mechatronic system to probe the algorithms underlying the optimization of energetic cost in walking. The system applies controlled fore-aft forces to a hip-belt worn by a user, decreasing their energetic cost by pulling forward, or increasing it by pulling backward. The system controls the forces, and thus energetic cost as a function of how the user is moving. In testing, we found that the system can quickly, accurately, and precisely apply target forces within a walking step. We next controlled the forces as a function of the user's step frequency and found that we could predictably reshape their energetic cost landscape. Finally, we tested whether users adapted their walking in response to the new cost landscapes created by our system, and found that users shifted their step frequency toward the new energetic minima. Our system design appears to be effective for reshaping energetic cost landscapes in human walking to study how the nervous system optimizes movement.

Is natural variability in gait sufficient to initiate spontaneous energy optimization in human walking?

In new walking contexts, the nervous system can adapt preferred gaits to minimize energetic cost. During treadmill walking, this optimization is not usually spontaneous but instead requires experience with the new energetic cost landscape. Experimenters can provide subjects with the needed experience by prescribing new gaits or instructing them to explore new gaits. Yet in familiar walking contexts, people naturally prefer energetically optimal gaits: the nervous system can optimize cost without an experimenter's guidance. Here we test the hypothesis that the natural gait variability of overground walking provides the nervous system with sufficient experience with new cost landscapes to initiate spontaneous minimization of energetic cost. We had subjects walk over paths of varying terrain while wearing knee exoskeletons that penalized walking as a function of step frequency. The exoskeletons created cost landscapes with minima that were, on average, 8% lower than the energetic cost at the initially preferred gaits and achieved at walking speeds and step frequencies that were 4% lower than the initially preferred values. We found that our overground walking trials amplified gait variability by 3.7-fold compared with treadmill walking, resulting in subjects gaining greater experience with new cost landscapes, including frequent experience with gaits at the new energetic minima. However, after 20 min and 2.0 km of walking in the new cost landscapes, we observed no consistent optimization of gait, suggesting that natural gait variability during overground walking is not always sufficient to initiate energetic optimization over the time periods and distances tested in this study. NEW & NOTEWORTHY While the nervous system can continuously optimize gait to minimize energetic cost, what initiates this optimization process during every day walking is unknown. Here we tested the hypothesis that the nervous system leverages the natural variability in gait experienced during overground walking to converge on new energetically optimal gaits created using exoskeletons. Contrary to our hypothesis, we found that participants did not adapt toward optimal gaits: natural variability is not always sufficient to initiate spontaneous energy optimization.

Scaling of sensorimotor delays in terrestrial mammals.

Whether an animal is trying to escape from a predator, avoid a fall or perform a more mundane task, the effectiveness of its sensory feedback is constrained by sensorimotor delays. Here, we combine electrophysiological experiments, systematic reviews of the literature and biophysical models to determine how delays associated with the fastest locomotor reflex scale with size in terrestrial mammals. Nerve conduction delay is one contributor, and increases strongly with animal size. Sensing, synaptic and neuromuscular junction delays also contribute, and we approximate each as a constant value independent of animal size. Muscle's electromechanical and force generation delays increase more moderately with animal size than nerve conduction delay, but their total contribution exceeds that of the four neural delays. The sum of these six component delays, termed total delay, increases with animal size in proportion to M0.21-large mammals experience total delays 17 times longer than small mammals. The slower movement times of large animals mostly offset their long delays resulting in a more modest, but perhaps still significant, doubling of their total delay relative to movement duration when compared with their smaller counterparts. Irrespective of size, sensorimotor delay is likely a challenge for all mammals, particularly during fast running.

Contribution of blood oxygen and carbon dioxide sensing to the energetic optimization of human walking.

People can adapt their gait to minimize energetic cost, indicating that walking's neural control has access to ongoing measurements of the body's energy use. In this study we tested the hypothesis that an important source of energetic cost measurements arises from blood gas receptors that are sensitive to O2 and CO2 concentrations. These receptors are known to play a role in regulating other physiological processes related to energy consumption, such as ventilation rate. Given the role of O2 and CO2 in oxidative metabolism, sensing their levels can provide an accurate estimate of the body's total energy use. To test our hypothesis, we simulated an added energetic cost for blood gas receptors that depended on a subject's step frequency and determined if subjects changed their behavior in response to this simulated cost. These energetic costs were simulated by controlling inspired gas concentrations to decrease the circulating levels of O2 and increase CO2 We found this blood gas control to be effective at shifting the step frequency that minimized the ventilation rate and perceived exertion away from the normally preferred frequency, indicating that these receptors provide the nervous system with strong physiological and psychological signals. However, rather than adapt their preferred step frequency toward these lower simulated costs, subjects persevered at their normally preferred frequency even after extensive experience with the new simulated costs. These results suggest that blood gas receptors play a negligible role in sensing energetic cost for the purpose of optimizing gait.NEW & NOTEWORTHY Human gait adaptation implies that the nervous system senses energetic cost, yet this signal is unknown. We tested the hypothesis that the blood gas receptors sense cost for gait optimization by controlling blood O2 and CO2 with step frequency as people walked. At the simulated energetic minimum, ventilation and perceived exertion were lowest, yet subjects preferred walking at their original frequency. This suggests that blood gas receptors are not critical for sensing cost during gait.