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 research is described in Science Robotics.
“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.”
When humans walk, we constantly tweak how we move to save energy (also known as metabolic cost).
The 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, 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 honed in on the best parameters, it directed the exosuit on when and where to deliver its assistive force to improve hip extension. The Bayesian Optimization approach used by the team was first reported in a paper last year in PLOSone.
The combination of the algorithm and suit reduced metabolic cost by 17.4% compared to walking without the device. This was a more than 60% 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.
