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Kinematics of the same individual during different bouts of walking

Human walking optimization describes gait and musculoskeletal changes that occur as an individual develops a consistent walking pattern.  Individuals make these changes to minimize some cost function, which is well-characterized by the energetic cost during steady-state level-ground walking, but can vary in non-steady-state walking and other kinds of locomotion. How and why humans adapt different gaits during locomotion is a common research topic in the fields of biomechanics, terrestrial locomotion, and rehabilitation engineering.

Cost function during walking

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During walking, humans are believed to optimize some cost function; this is a representation of one or more variables that define the individual's internal model of the "cost" associated with locomotion. Determining the exact variables and weights that make up an individual's cost function is difficult, as this may change with locomotion type (walking, running, etc.), fatigue, injuries, external influences, and several other variables.

Energetic cost

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Across various types of biomechanical studies during steady-state walking, humans choose to walk in a way that minimizes their energetic cost[1]. However, the exact physiological mechanism for how humans sense their energy expenditure has not been determined. Previous work found that blood gas receptors were not responsible for informing energetic minimization during gait, but suggest that afferent receptors and proprioceptive feedback could be other physiological sensors for energetic cost during locomotion[2][3].

However, recent work has shown that energetic cost may not entirely explain an individual's choice of gait pattern, even during steady-state walking. This work revealed that individuals' preferred gait patterns are not always accompanied by the lowest energy expenditure, suggesting that energy expenditure may not be the only variable in an individual's cost function during steady-state walking[4].

Stability

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Walking stability can also play a role in an individual's cost function when choosing an optimal gait. For example, in downhill walking the force of gravity can help an individual reduce their energetic cost by reducing the need for push-off power to drive forward propulsion. However, utilizing gravity in this way also reduces stability. Individuals have been shown to balance the energetic "pros" and stability "cons" from gravity when choosing their walking pattern, causing them to walk at a gait that is not their energetic minimum[5].

Understanding the role of stability in an individual's cost function can be challenging because the biomechanics field does not have a single best metric for analyzing stability. Some popular stability metrics are based on the inverted pendulum model and extrapolated center of mass concept, which is paralleled by the capture point concept in the bipedal robotics field[6][7]. Another popular stability metric is whole body angular momentum, which accounts for individual body segments' contributions to momentum about the center of mass[8].

Time

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The amount of time given to walk some distance can also influence the walking parameters that an individual chooses. If given a set amount of time to walk some distance, humans will often walk faster than necessary, even when the time given allows them to walk at the same speed or slower than their preferred walking speed[9]. Other approaches have evaluated how humans balance the consideration of time and energetic cost. When individuals were given a choice between walking some distance and running some distance, individuals' preferences varied[10]. Some individuals minimized energetic costs, some minimized time to complete the task, and the remaining participants did not seem to prioritize a single outcome, but rather balance the two, showing that neither time nor energetic cost is the single most influential in determining locomotion behavior[10]. This area of research differs from many controlled "in-lab" experiments because it requires individuals to make a prediction about what their energetic cost would be over some period of time or distance, where other applications allow individuals to instantaneously modify their gait with no temporal considerations.

Optimization to different environments

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Split-belt treadmills are used to study gait adaptations in human walking.

Split-belt treadmills

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Split-belt treadmills are often used to study motor adaptation because they can induce different treadmill belt speeds under each foot, causing humans to adjust their inter-limb coordination relative to normal walking. Even in these novel environments, individuals' adaptation over time causes a reduction in energetic cost[11]. Research has shown that humans will naturally harness energy from a split-belt treadmill in order to reduce their energetic cost of walking[12][13][14].

Wearable devices

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A large portion of exoskeleton and lower-limb prosthetics research has aimed to reduce the energetic cost of walking[15]. During steady-state level-ground walking with exoskeleton devices, humans have been shown to walk in the most energetically optimal manner. This consistent human behavior has enabled an area of exoskeleton research, human-in-the-loop optimization, that uses experimental energetic cost to optimize a wide array of exoskeleton parameters, commonly through the use of covariance matrix adaptation evolution strategy and Bayesian optimization[16][17]. Human-in-the-loop optimization has enabled the creation of complex robotic devices that have reduced the energetic cost of walking by up to 50%[18][19].

 
Example of an altered energetic cost landscape, adapted from Selinger et al. 2019[20]

Artificially-altered metabolic cost

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Researchers have used wearable exoskeletons and other external devices to reshape the human walking energetic cost landscape. When individuals are exposed to this new landscape, some continue to walk in their normal gait pattern, which is now energetically sub-optimal in the altered landscape[21][22]. However, once these individuals were exposed to the rest of the cost landscape, they began walking at the energetically optimal gait pattern[21][22]. This suggests that humans may need to be broadly exposed to some metabolic cost landscapes, notably those that differ from their natural cost landscape, before they choose the gait pattern that minimizes their energetic cost.

Optimization by altering step characteristics

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Humans are able to modulate their gait by altering their step characteristics. These spatiotemporal changes can be used to minimize energetic cost, improve stability, or optimize other cost functions during locomotion.

Step Frequency

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Several studies have focused on altering the step frequency of the participant to study adaptation during walking. Step frequency is often used as an experimental variable due to the ease of controlling it by having participants match their steps to the frequency of a metronome. Humans typically adjust their step frequency to minimize their energetic cost. However, this assumption was shown not to be true in some situations, such as during barefoot walking where the scientists hypothesized that individuals may have been prioritizing comfort over minimizing energetic cost[23]. Additionally, increased step frequencies have been shown to increase stability, which could be another cause for deviation from an energetic minimum step frequency[24]. It is worth noting that, in fixed-speed experiments that often take place on treadmills in laboratory settings, step frequency and step length must vary in tandem to maintain the fixed speed.

Step Length

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Step length is often thought of as being dictated by passive dynamics, especially during steady-state locomotion. An individual's preferred gait pattern uses a combination of step length and step frequency to achieve some speed, which optimizes the energetic cost of walking[25]. Increases in preferred step length have severe negative impacts on energetic cost due to increased collision forces and required push-off work[26]. However, shorter step lengths increase stability, though they are constrained by some feasible range of step frequencies to achieve the desired walking speed[27][24].

Step Width

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Humans typically walk with the step width that minimizes their energetic cost[28]. Walking with wider than preferred steps increases the energetic cost of transitioning from one foot to another, while walking with narrower than preferred steps requires additional energy to keep the swing leg path closer to the body[28]. However, in unstable environments, humans widen their step width to increase stability through creating wider base of support, though this does have a negative impact on their energetic cost[29].

See also

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References

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  1. ^ Summerside, Erik M.; Kram, Rodger; Ahmed, Alaa A. (2018-06). "Contributions of metabolic and temporal costs to human gait selection". Journal of the Royal Society, Interface. 15 (143). doi:10.1098/rsif.2018.0197. ISSN 1742-5662. PMC 6030640. PMID 29925582. {{cite journal}}: Check date values in: |date= (help)
  2. ^ Wong, Jeremy D.; O’Connor, Shawn M.; Selinger, Jessica C.; Donelan, J. Maxwell (2017-06-21). "Contribution of blood oxygen and carbon dioxide sensing to the energetic optimization of human walking". Journal of Neurophysiology. 118 (2): 1425–1433. doi:10.1152/jn.00195.2017. ISSN 0022-3077. PMC 5558034. PMID 28637813.{{cite journal}}: CS1 maint: PMC format (link)
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  4. ^ Antos, Stephen A.; Kording, Konrad P.; Gordon, Keith E. (2021-04-12). "Energy expenditure does not explain step length-width choices during walking". bioRxiv: 2021.04.11.439375. doi:10.1101/2021.04.11.439375.
  5. ^ "The cost of walking downhill: Is the preferred gait energetically optimal?". Journal of Biomechanics. 43 (10): 1910–1915. 2010-07-20. doi:10.1016/j.jbiomech.2010.03.030. ISSN 0021-9290.
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  7. ^ Pratt, Jerry; Carff, John; Drakunov, Sergey; Goswami, Ambarish (2006-12-XX). "Capture Point: A Step toward Humanoid Push Recovery". 2006 6th IEEE-RAS International Conference on Humanoid Robots: 200–207. doi:10.1109/ICHR.2006.321385. {{cite journal}}: Check date values in: |date= (help)
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  14. ^ Selgrade, Brian P.; Thajchayapong, Montakan; Lee, Gloria E.; Toney, Megan E.; Chang, Young-Hui (2017-08-15). "Changes in mechanical work during neural adaptation to asymmetric locomotion". The Journal of Experimental Biology. 220 (Pt 16): 2993–3000. doi:10.1242/jeb.149450. ISSN 1477-9145. PMC 5576064. PMID 28596214.
  15. ^ Sawicki, Gregory S.; Beck, Owen N.; Kang, Inseung; Young, Aaron J. (2020-02-19). "The exoskeleton expansion: improving walking and running economy". Journal of NeuroEngineering and Rehabilitation. 17 (1): 25. doi:10.1186/s12984-020-00663-9. ISSN 1743-0003. PMC 7029455. PMID 32075669.{{cite journal}}: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link)
  16. ^ Kim, Myunghee; Ding, Ye; Malcolm, Philippe; Speeckaert, Jozefien; Siviy, Christoper J.; Walsh, Conor J.; Kuindersma, Scott (2017-09-19). "Human-in-the-loop Bayesian optimization of wearable device parameters". PLOS ONE. 12 (9): e0184054. doi:10.1371/journal.pone.0184054. ISSN 1932-6203. PMC 5604949. PMID 28926613.{{cite journal}}: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link)
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  19. ^ Bryan, Gwendolyn M.; Franks, Patrick W.; Song, Seungmoon; Voloshina, Alexandra S.; Reyes, Ricardo; O’Donovan, Meghan P.; Gregorczyk, Karen N.; Collins, Steven H. (2021-03-28). "Optimized hip-knee-ankle exoskeleton assistance at a range of walking speeds". bioRxiv: 2021.03.26.437212. doi:10.1101/2021.03.26.437212.
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  28. ^ a b Donelan, J. M.; Kram, R.; Kuo, A. D. (2001-10-07). "Mechanical and metabolic determinants of the preferred step width in human walking". Proceedings of the Royal Society B: Biological Sciences. 268 (1480): 1985–1992. doi:10.1098/rspb.2001.1761. ISSN 0962-8452. PMC 1088839. PMID 11571044.
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