In an intriguing leap forward for biomechanics and robotics, a team of researchers has shed light on how humans maintain stability while adapting to new walking environments. The study, recently published in Nature Communications, presents a groundbreaking model that captures the nuances of locomotion adaptation, according to MIT's Department of Brain and Cognitive Sciences assistant professor Nidhi Seethapathi, Bright Minds Inc. robotics software engineer Barrett C. Clark, and Ohio State University's Department of Mechanical and Aerospace Engineering associate professor Manoj Srinivasan.
The trio's research contrasts the nature of episodic tasks, like reaching for an item, with continuous activities such as walking. While the former's errors are contained within single episodes, in locomotion, errors without correction could cascade, affecting current and future stability, making adaptation within this sphere significantly more complex. "This new theoretical model captures adaptation phenomena in continuous long-horizon tasks in multiple locomotor settings," Seethapathi told MIT News.
Building this model required distilling general principles of how humans adapt their walking across various settings. The researchers' unified approach resulted in a model modular in design yet hierarchical. It boasts a mathematical framework specific to each component, which allows it to encapsulate and predict human adaptation in unfamiliar locomotion settings effectively.
Validating their model, the team's efforts successfully reproduced past human adaptation phenomena, such as walking on a split-belt treadmill or wearing uneven leg weights. And, equally important, the model foresaw the adaptive behaviors recorded in two novel experiments, a testament to its predictive power. "You can think of a wearable robot itself as a new environment for the person to move in, and our model can be used to predict how a person will adapt for different robot settings," Seethapathi explained in the MIT News article.
