Assistive devices like exosuits are being manufactured by various developers including the Harvard Biodesign Lab. In order to make these soft, assistive devices work properly, the wearer and the robot need to be in sync. However, since every human moves in a different style, tailoring a robot to fit the need of an individual user is time consuming and inefficient process.
Now, researchers from the Harvard University have developed an efficient machine learning algorithm that can rapidly tailor personalized control strategies for soft, wearable exosuits, remarkably enhancing the performance of the device.
The research team was 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.
“This new method is an effective and fast way to optimize control parameter settings for assistive wearable devices,” said co-first author Ye Ding, a postdoctoral fellow at John A. Paulson School of Engineering and Applied Sciences (SEAS). “Using this method, we achieved a huge improvement in metabolic performance for the wearers of a hip extension assistive device.”
When we walk, we continuously tweak how we move in order to save energy, which in medical term, is known as metabolic cost.
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