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Harvard researchers have developed an efficient machine learning algorithm that can quickly tailor personalized control strategies for soft, wearable exosuits, significantly improving the performance of the device. (Credit: Ye Ding/Harvard SEAS)

When it comes to soft, assistive devices — like the exosuit being designed by the Harvard Biodesign Lab — the wearer and the robot need to be in sync. But every human moves a bit differently and tailoring the robot's parameters for an individual user is a time-consuming and inefficient process.

Now, researchers from the Harvard John A. Paulson School of Engineering and Applied and Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering have developed an efficient machine learning algorithm that can quickly tailor personalized control strategies for soft, wearable exosuits.

The researchers developed an algorithm that can cut through that variability and rapidly identify the best control parameters that work best for minimizing the of walking. The researchers used 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 homed in on the best parameters, it directed the exosuit on when and where to deliver its assistive force to improve hip extension.

The combination of the algorithm and suit reduced metabolic cost by 17.4 percent compared to walking without the device. This was a more than 60 percent improvement compared to the team's previous work.

Next, the team aims 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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