Researchers Develop Human-in-the-Loop Optimization of Hip Assistance with a Soft Exosuit During Walking

Sunday, March 4, 2018 - 09:36

Researchers from the Harvard John A. Paulson School of Engineering and Applied and Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering at Harvard University have developed an efficient machine-learning algorithm that can do that work quickly.

Human beings constantly tweak their movements to save energy while walking. Researchers call this the metabolic cost, the university official website reports.

“This new method is an effective and fast way to optimize control parameter settings for assistive wearable devices,” said Ye Ding, a postdoctoral fellow at SEAS and co-first author of the research. “Using this method, we achieved a huge improvement in metabolic performance for the wearers of a hip extension assistive device.”

The new algorithm, developed by researchers led by Conor Walsh, the John L. Loeb Associate Professor of Engineering and Applied Sciences, and Scott Kuindersma, assistant professor of engineering and computer science at SEAS, cuts through that variability to rapidly identify the best control parameters to minimize the work of walking.

The researchers applied so-called human-in-the-loop optimization, which uses real-time measurements of human physiological signals, such as breathing rate, to adjust the control parameters of the device. As the algorithm honed in on the best parameters, it told the exosuit when and where to deliver its assistive force to improve hip extension. The Bayesian Optimization approach the team used was first reported in a paper last year in PLOSone.

“Optimization and learning algorithms will have a big impact on future wearable robotic devices designed to assist a range of behaviors,” said Kuindersma. “These results show that optimizing even very simple controllers can provide a significant, individualized benefit to users while walking. Extending these ideas to consider more expressive control strategies and people with diverse needs and abilities will be an exciting next step.”

The team’s next step is to apply the optimization to a more complex device that assists multiple joints, such as hip and ankle, at the same time.

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