Personalising wearables with human-in-the-loop optimisation

Now, researchers from the Wyss Institute for Biologically Inspired Engineering and the Harvard John A. Paulson School of Engineering and Applied and Sciences (SEAS) have developed an efficient machine learning algorithm that can quickly tailor personalised control strategies for soft, wearable exosuits. The research is described in Science Robotics.

“This new method is an effective and fast way to optimise 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.”

When humans walk, we constantly tweak how we move to save energy (also known as metabolic cost).

“Before, if you had three different users walking with assistive devices, you would need three different assistance strategies,” said Myunghee Kim, Ph.D., postdoctoral research fellow at SEAS and co-first author of the paper.

“Finding the right control parameters for each wearer used to be a difficult, step-by-step process because not only do all humans walk a little differently but the experiments required to manually tune parameters are complicated and time consuming.”

The researchers, led by Conor Walsh, Ph.D., Core Faculty member at the Wyss Institute and the John L. Loeb Associate Professor of Engineering and Applied Sciences, and Scott Kuindersma, Ph.D., Assistant Professor of Engineering and Computer Science at SEAS, developed an algorithm that can cut through that variability and rapidly identify the best control parameters that work best for minimising the energy used for walking.

The researchers used so-called human-in-the-loop optimisation, 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 Bayesian Optimisation approach used by the team was first report in a paper in PLOS ONE.

The combination of the algorithm and suit reduced metabolic cost by 17.4% compared to walking without the device. This was a more than 60% improvement compared to the team’s previous work.

“Optimisation and learning algorithms will have a big impact on future wearable robotic devices designed to assist a range of behaviours,” said Kuindersma. “These results show that optimising even very simple controllers can provide a significant, individualised benefit to users while walking. Extending these ideas to consider more expressive control strategies and people with diverse needs and abilities will be an exciting next step.”

“With wearable robots like soft exosuits, it is critical that the right assistance is delivered at the right time so that they can work synergistically with the wearer,” said Walsh. “With these online optimisation algorithms, systems can learn how do achieve this automatically in about twenty minutes, thus maximising benefit to the wearer.”

Next, the team aims to apply the optimisation to a more complex device that assists multiple joints, such as hip and ankle, at the same time.

Discover more here.

Image credit: Wyss Institute.