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. When humans walk, we constantly tweak how we move to save energy (also known as metabolic cost).
Now, researchers from the Wyss Institute for Biologically Inspired Engineering and the Harvard John A. Paulson School of Engineering and Applied and Sciences (SEAS) have developed an efficient machine learning algorithm that can quickly tailor personalized control strategies for soft, wearable exosuits.
“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 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 honed 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.
“With wearable robots like soft exosuits, it is critical that the right assistance is delivered at the right time so that they can work synergistically with the wearer,” said Walsh. “With these online optimization algorithms, systems can learn how do achieve this automatically in about twenty minutes, thus maximizing benefit to the wearer.”
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.