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.
“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.”
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