Personalizing wearable devices

When it comes to soft, assistive devices - like the exosuit being designed 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 for an individual user is a time-consuming and inefficient process. Now, 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 research is described in Science Robotics. "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." 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, postdoctoral research fellow at SEAS and co-first author of the paper.