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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Every Wednesday is Wearable Wednesday here at Adafruit! We’re bringing you the blinkiest, most fashionable, innovative, and useful wearables from around the web and in our own original projects featuring our wearable Arduino-compatible platform, FLORA. Be sure to post up your wearables projects in the forums or send us a link and you might be featured here on Wearable Wednesday!