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Humans Consistently Adapt for Stable, Balanced Walking

Continuous adaptation of humans during intricate activities, such as walking, ensures stability, according to a novel MIT model.

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Continuous adaptation during complex tasks, such as walking, ensures human stability, as...
Continuous adaptation during complex tasks, such as walking, ensures human stability, as demonstrated by a novel model from MIT.

Humans Consistently Adapt for Stable, Balanced Walking

Band of researchers have engineered a theory that cracked the code on how people self-adjust during tricky tasks, like taking a stroll, while keeping their balance.

Their discovery was detailed in a recent paper published in the magazine Nature Communications, penned by Nidhi Seethapathi, an assistant professor in MIT's Department of Brain and Cognitive Sciences; Barrett C. Clark, a robotics software engineer at Bright Minds Inc.; and Manoj Srinivasan, an associate professor in the Department of Mechanical and Aerospace Engineering at Ohio State University.

In episodic tasks, such as grabbing an object, mistakes in one task won't affect the next - it's all contained. However, in tasks like walking, errors can snowball into both short-term and long-term wobbliness challenges unless controlled. This makes adapting locomotion in new scenarios a far more complex endeavor.

"Prior theoretical understanding of adapting has been restricted to episodic tasks, for example, reaching for an object in a fresh environment," Seethapathi notes. "This new theoretical model takes into account adapting phenomena in continuous long-term tasks across various locomotor settings."

To build their model, the researchers pinned down general principles of locomotor adaptation across numerous task settings and developed a combined modular and hierarchical model for locomotor adaptation. Each component of the model had a distinct mathematical role.

The outcome showed how people alter their walking in new scenarios such as walking on a split-belt treadmill with each foot moving at a different pace, sporting uneven leg weights, and wearing an exoskeleton. The researchers reported that the model accurately mimicked human locomotor adaptation phenomena across different settings in 10 prior studies and accurately predicted the adaptation behavior observed in two brand-new experiments during the study.

The model has the potential for use in sensorimotor learning, rehabilitation, and wearable robotics.

"A model that can forecast how a person adapts to a new environment is gold for creating better rehabilitation routines and wearable robot control," Seethapathi says. "Imagine a wearable robot as a new environment for the individual to move through, and our model can be applied to forecast how an individual will adapt for various robot settings. Figuring out human-robot adaptation is a pricey and time-consuming process, and our model could lessen the time needed by trimming down possible scenarios."

Although the research paper has not been fully dissected in the results, one might speculate that it delves into neural mechanisms, muscle adjustments, or cognitive strategies that drive performance and learning improvement in locomotor tasks. It could also investigate the impact of task complexity, motor control systems, and cognitive-motor functions on adaptation.

Potential applications includes enhancements in rehabilitation strategies for those with motor disorders or injuries, robotics, sports training, and neuroprosthetic devices and assistive technology. Nevertheless, it's crucial to mention that these speculations are based on existing knowledge of locomotor adaptation and require access to the specific Nature Communications paper or communication with the authors themselves for precision.

  1. The researchers' discovery about self-adjustment during tricky tasks was published in the journal Nature Communications.
  2. Nidhi Seethapathi, an assistant professor in MIT's Department of Brain and Cognitive Sciences, was one of the authors who penned the article.
  3. The research paper details a new theoretical model that takes into account adapting phenomena in continuous long-term tasks across various locomotor settings.
  4. The model is built on general principles of locomotor adaptation across numerous task settings and comprises a combined modular and hierarchical model for locomotor adaptation.
  5. The model has the potential for use in areas like sensorimotor learning, rehabilitation, and wearable robotics.
  6. One might speculate that the research paper delves into neural mechanisms, muscle adjustments, or cognitive strategies that drive performance and learning improvement in locomotor tasks.
  7. Possible applications of the model include enhancements in rehabilitation strategies for those with motor disorders or injuries, robotics, sports training, and neuroprosthetic devices and assistive technology.
  8. Further research and access to the Nature Communications paper are necessary to validate these speculations and understand the full implications of the model in fields such as environmental science, medical-conditions, health-and-wellness, fitness-and-exercise, and space-and-astronomy.
  9. The study's findings could contribute significantly to education-and-self-development, technology, and the broader field of science by providing a deeper understanding of the adaptive processes involved in various mental, physical, and environmental tasks.

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