A mechatronic system for studying energy optimization during walking.

Mapping Intimacies ◽

10.1101/496489 ◽

2018 ◽

Cited By ~ 1

Author(s):

Surabhi N Simha ◽

J. Maxwell Donelan

Keyword(s):

Nervous System ◽

System Design ◽

Human Movement ◽

Human Walking ◽

Energetic Cost ◽

Mechatronic System ◽

Step Frequency ◽

Coordination Strategies ◽

Design Build ◽

Optimal Coordination

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 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 towards 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.

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

Journal of Neurophysiology ◽

10.1152/jn.00311.2020 ◽

2021 ◽

Author(s):

Surabhi N Simha ◽

Jeremy D. Wong ◽

Jessica C Selinger ◽

Sabrina J Abram ◽

J. Maxwell Donelan

Keyword(s):

Nervous System ◽

Lower Cost ◽

Cost Savings ◽

Control Policy ◽

Human Walking ◽

Energetic Cost ◽

Mechatronic System ◽

Step Frequency ◽

Normalized Units ◽

New Situation

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.

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

10.1101/2020.05.20.107250 ◽

2020 ◽

Author(s):

Surabhi N. Simha ◽

Jeremy D. Wong ◽

Jessica C. Selinger ◽

Sabrina J. Abram ◽

J. Maxwell Donelan

Keyword(s):

Nervous System ◽

Lower Cost ◽

Cost Savings ◽

Control Policy ◽

Human Walking ◽

Energetic Cost ◽

Mechatronic System ◽

Step Frequency ◽

Normalized Units ◽

New Situation

AbstractWhen 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.

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How People Initiate Energy Optimization and Converge on Their Optimal Gaits

10.1101/503433 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jessica Selinger ◽

Jeremy Wong ◽

Surabhi Simha ◽

Maxwell Donelan

Keyword(s):

Nervous System ◽

Reinforcement Learning ◽

Energy Optimization ◽

Coordination Strategies ◽

Human Experiments ◽

Optimal Coordination ◽

Optimal Gait ◽

Reinforcement Learning Models ◽

The Cost ◽

The Relationship

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 model this optimization process as reinforcement learning and find behavior that closely matches these experimental observations. 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.

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

Journal of Neurophysiology ◽

10.1152/jn.00195.2017 ◽

2017 ◽

Vol 118 (2) ◽

pp. 1425-1433 ◽

Cited By ~ 7

Author(s):

Jeremy D. Wong ◽

Shawn M. O’Connor ◽

Jessica C. Selinger ◽

J. Maxwell Donelan

Keyword(s):

Perceived Exertion ◽

Human Walking ◽

Human Gait ◽

Energetic Cost ◽

Blood Oxygen ◽

Blood Gas ◽

Step Frequency ◽

Gait Adaptation ◽

Gait Optimization ◽

Sensing Cost

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.

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Energy Optimization is a Major Objective in the Real-Time Control of Step Width in Human Walking

10.1101/506808 ◽

2018 ◽

Author(s):

Sabrina J. Abram ◽

Jessica C. Selinger ◽

J. Maxwell Donelan

Keyword(s):

Nervous System ◽

Real Time ◽

Energy Optimization ◽

Energetic Cost ◽

Step Width ◽

Real Time Control ◽

Step Frequency ◽

Major Objective ◽

Cost Shifting ◽

Time Control

People prefer to move in energetically optimal ways during walking. We have 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 test whether energy optimization is also a major objective in the nervous system's real-time control of step width. To accomplish this, we use a device that can reshape the relationship between step width and energetic cost, shifting the energy optimal width wider than that initially preferred. We find that the nervous system doesn't spontaneously initiate energy optimization, but instead requires experience with a lower energetic cost step width. After initiating optimization, people converge towards their new energy optimal width within hundreds of steps and update this as their new preferred width, rapidly returning to it when perturbed away. However, energy optimization was incomplete as this new preferred width was slightly narrower than the energetically optimal width. This suggests that the nervous system may determine its preferred width by optimizing energy simultaneously with other objectives such as stability or maneuverability. Collectively, these findings indicate that the nervous systems of able-bodied people continuously optimize energetic cost to determine preferred step width.

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

Journal of Neurophysiology ◽

10.1152/jn.00417.2018 ◽

2019 ◽

Vol 121 (5) ◽

pp. 1848-1855 ◽

Cited By ~ 2

Author(s):

Jeremy D. Wong ◽

Jessica C. Selinger ◽

J. Maxwell Donelan

Keyword(s):

Nervous System ◽

Energy Optimization ◽

Natural Variability ◽

Gait Variability ◽

Treadmill Walking ◽

Energetic Cost ◽

Overground Walking ◽

Step Frequency ◽

Time Periods ◽

Walking Speeds

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.

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Trial-by-Trial Adaptation of Movements during Mental Practice under Force Field

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/109497 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Muhammad Nabeel Anwar ◽

Salman Hameed Khan

Keyword(s):

Nervous System ◽

Force Field ◽

Motor Imagery ◽

Internal Model ◽

Human Movement ◽

Mental Practice ◽

Higher Learning ◽

Reaching Movements ◽

Adaptive Compensation ◽

Human Nervous System

Human nervous system tries to minimize the effect of any external perturbing force by bringing modifications in the internal model. These modifications affect the subsequent motor commands generated by the nervous system. Adaptive compensation along with the appropriate modifications of internal model helps in reducing human movement errors. In the current study, we studied how motor imagery influences trial-to-trial learning in a robot-based adaptation task. Two groups of subjects performed reaching movements with or without motor imagery in a velocity-dependent force field. The results show that reaching movements performed with motor imagery have relatively a more focused generalization pattern and a higher learning rate in training direction.

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