Predictive Simulations to Replicate Human Gait Adaptations and Energetics With Exoskeletons.

Robotic exoskeletons have the potential to restore and enhance human mobility. However, optimally controlling these devices, to work in concert with human users, is challenging. Accurate model simulations of the interaction between exoskeletons and users may expedite the design process and improve control. Here, as a proof of principle, we tested if we could use predictive simulations to replicate human gait adaptations and changes in energy expenditure from an experiment where participants walked with exoskeletons. We recreated a past experimental paradigm, where robotic exoskeletons were used to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. To match the experimental controller, we modelled knee-worn exoskeletons that applied resistive torques, either proportional or inversely proportional to step frequency-decreasing or increasing the energy optimal step frequency, respectively. We were able to replicate the experiment, finding higher and lower optimal step frequencies than in natural walking under each respective condition. Our simulated resistive torques and objective landscapes resembled the measured experimental resistive torque and energy landscapes. Individual muscle energetics revealed distinct coordination strategies consistent with each exoskeleton controller condition. Increasing the accuracy of step frequency and energetic predictions was best achieved by increasing the number of virtual participants (varying whole-body anthropometrics), rather than the number of muscle parameter sets (varying muscle anthropometrics). In future, our approach can be used to design controllers in advance of human testing, to help identify reasonable solution spaces or tailor design to individual users.

Running in the wild: Energetics explain ecological running speeds.

Human runners have long been thought to have the ability to consume a near-constant amount of energy per distance traveled, regardless of speed, allowing speed to be adapted to particular task demands with minimal energetic consequence.1-3 However, recent and more precise laboratory measures indicate that humans may in fact have an energy-optimal running speed.4-6 Here, we characterize runners' speeds in a free-living environment and determine if preferred speed is consistent with task- or energy-dependent objectives. We analyzed a large-scale dataset of free-living runners, which was collected via a commercial fitness tracking device, and found that individual runners preferred a particular speed that did not change across commonly run distances. We compared the data from lab experiments that measured participants' energy-optimal running speeds with the free-living preferred speeds of age- and gender-matched runners in our dataset and found the speeds to be indistinguishable. Human runners prefer a particular running speed that is independent of task distance and is consistent with the objective of minimizing energy expenditure. Our findings offer an insight into the biological objectives that shape human running preferences in the real world-an important consideration when examining human ecology or creating training strategies to improve performance and prevent injury.

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.

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.

Connecting the legs with a spring improves human running economy.

Human running is inefficient. For every 10 calories burned, less than 1 is needed to maintain a constant forward velocity - the remaining energy is, in a sense, wasted. The majority of this wasted energy is expended to support the bodyweight and redirect the center of mass during the stance phase of gait. An order of magnitude less energy is expended to brake and accelerate the swinging leg. Accordingly, most devices designed to increase running efficiency have targeted the costlier stance phase of gait. An alternative approach is seen in nature: spring-like tissues in some animals and humans are believed to assist leg swing. While it has been assumed that such a spring simply offloads the muscles that swing the legs, thus saving energy, this mechanism has not been experimentally investigated. Here, we show that a spring, or 'exotendon', connecting the legs of a human reduces the energy required for running by 6.4±2.8%, and does so through a complex mechanism that produces savings beyond those associated with leg swing. The exotendon applies assistive forces to the swinging legs, increasing the energy optimal stride frequency. Runners then adopt this frequency, taking faster and shorter strides, and reduce the joint mechanical work to redirect their center of mass. Our study shows how a simple spring improves running economy through a complex interaction between the changing dynamics of the body and the adaptive strategies of the runner, highlighting the importance of considering each when designing systems that couple human and machine.

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.

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.

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.

Myoelectric Control for Adaptable Biomechanical Energy Harvesting.

We have designed and tested a myoelectric controller that automatically adapts energy harvesting from the motion of leg joints to match the power available in different walking conditions. To assist muscles in performing negative mechanical work, the controller engages power generation only when estimated joint mechanical power is negative. When engaged, the controller scales its resistive torque in proportion to estimated joint torque, thereby automatically scaling electrical power generation in proportion to the available mechanical power. To produce real-time estimates of joint torque and mechanical power, the controller leverages a simple model that predicts these variables from measured muscle activity and joint angular velocity. We first tested the model using available literature data for a range of walking speeds and found that estimates of knee joint torque and power well match the corresponding literature profiles (torque R(2): 0.73-0.92; power R(2): 0.60-0.94). We then used human subject experiments to test the performance of the entire controller. Over a range of steady state walking speeds and inclines, as well as a number of non-steady state walking conditions, the myoelectric controller accurately identified when the knee generated negative mechanical power, and automatically adjusted the magnitude of electrical power generation.

Humans Can Continuously Optimize Energetic Cost during Walking.

People prefer to move in ways that minimize their energetic cost. For example, people tend to walk at a speed that minimizes energy use per unit distance and, for that speed, they select a step frequency that makes walking less costly. Although aspects of this preference appear to be established over both evolutionary and developmental timescales, it remains unclear whether people can also optimize energetic cost in real time. Here we show that during walking, people readily adapt established motor programs to minimize energy use. To accomplish this, we used robotic exoskeletons to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. In response, we found that subjects adapted their step frequency to converge on the new energetic optima within minutes and in response to relatively small savings in cost (<5%). When transiently perturbed from their new optimal gait, subjects relied on an updated prediction to rapidly re-converge within seconds. Our collective findings indicate that energetic cost is not just an outcome of movement, but also plays a central role in continuously shaping it.

"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost.

This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body "in-the-loop") and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects' preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.

Estimating instantaneous energetic cost during non-steady-state gait.

Respiratory measures of oxygen and carbon dioxide are routinely used to estimate the body's steady-state metabolic energy use. However, slow mitochondrial dynamics, long transit times, complex respiratory control mechanisms, and high breath-by-breath variability obscure the relationship between the body's instantaneous energy demands (instantaneous energetic cost) and that measured from respiratory gases (measured energetic cost). The purpose of this study was to expand on traditional methods of assessing metabolic cost by estimating instantaneous energetic cost during non-steady-state conditions. To accomplish this goal, we first imposed known changes in energy use (input), while measuring the breath-by-breath response (output). We used these input/output relationships to model the body as a dynamic system that maps instantaneous to measured energetic cost. We found that a first-order linear differential equation well approximates transient energetic cost responses during gait. Across all subjects, model fits were parameterized by an average time constant (τ) of 42 ± 12 s with an average R(2) of 0.94 ± 0.05 (mean ± SD). Armed with this input/output model, we next tested whether we could use it to reliably estimate instantaneous energetic cost from breath-by-breath measures under conditions that simulated dynamically changing gait. A comparison of the imposed energetic cost profiles and our estimated instantaneous cost demonstrated a close correspondence, supporting the use of our methodology to study the role of energetics during locomotor adaptation and learning.

Case study: a novel biomechanical approach for evaluating extended body armor systems.

To combat the devastating effects of improvised explosive devices (IEDs), body armor that provides extended coverage has been developed. However, this extended coverage increases the armor's weight and may restrict movement. Throughout this case study, a novel technique to assess several armor systems was investigated. Four soldiers performed shoulder and trunk movements while wearing each of the six different armor inserts. Electromyography (EMG) was used to quantify muscular activity and inertial motion sensors were used to determine joint range of motion (ROM). Outcome measures included maximum ROM, integrated EMG, and the soldiers' subjective rankings. For the shoulder tasks, objective ROM and EMG measures were related to each other as well as to subjective rankings and armor material properties. Conversely, little agreement was found between measures for the trunk tasks. Results of this preliminary investigation indicate that combining shoulder ROM and EMG measures has the potential to provide an objective assessment of body armor systems.

Effect of backpack load placement on posture and spinal curvature in prepubescent children.

Parents, educators and researchers have expressed concern about the long term impacts of children carrying excessive loads in their backpacks on a daily basis. Although many researchers have investigated appropriate weight limits for children's packs, little research has been conducted on the design of children's backpacks. The purpose of this study was to evaluate the changes in children's trunk forward lean (TFL), cranio-vertebral angle (CVA) and spinal lordosis angle (LA) that occurred with high, medium and low load locations during standing and walking. Ten-year-old children (n = 15) completed a repeated measures designed study while carrying 15% of each child's body weight in a typical backpack with only shoulder straps. A special instrumented backpack (IBP) was designed that allowed the weight to be placed in the proper location and continuously measure changes in spinal curvature. TFL and CVA postures were captured on digital video at five intervals including: standing without a backpack prior to a 1000 m walk; standing with a backpack at the beginning and end of a 1000 m walk; and walking with a backpack at the beginning and end of a 1000 m walk. Results indicated that significant changes occurred in TFL and CVA when the backpack was loaded to 15% body weight. The low load placement in the backpack produced fewer changes in CVA from the initial standing baseline measure than the high and mid placements. When all measures were assessed collectively, there were fewer changes in LA in the low load placement. These findings indicate that future backpack designs should place loads lower on the spine in order to minimize children's postural adaptations.