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 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 combination of the algorithm and
suit reduced metabolic cost by 17.4 percent compared to walking without the
device. This was a more than 60 percent 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.
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