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HIL Bayesian Optimization, Utilizing Machine Learning for Personalization #WearableWednesday

From the Harvard Gazette:

When it comes to soft assistive devices — like the wearable exosuit being created 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 to 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 at Harvard University have developed an efficient machine-learning algorithm that can do that work quickly.

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.”

Read more from the Harvard Gazette or Science Robotics and see more from Harvard John A. Paulson School of Engineering and Applied Sciences on YouTube


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