Reliable environmental is in context prediction is critical for wearable Robotics solutions, such as prosthetics, to assist with the terrain-adaptive locomotion. This is because lower-limb robotic prosthetics need to be execute different behaviors depending on where the user is walking and what the terrain is like.
“The framework we’ve created allows the AI in Robotic prostheses to predict the type of terrain users will be stepping on, quantify the uncertainties associated with that prediction, and then incorporate that uncertainty into its decision-making,” said Edgar Lobaton, co-author of the team’s paper and associate professor of electrical and computer engineering at NC State.
In their research, the NC State team focused on distinguishing between the top six key terrains that require adjustments in prosthetic behavior—‘tile’, ‘brick’, ‘concrete’, ‘grass’, ‘upstairs’, and ‘downstairs.’ “If the degree of uncertainty is too high, the Artificial intelligence (AI) isn’t forced to make a questionable decision – it could instead notify the user that it doesn’t have enough confidence in its prediction to act, or it could be default to a ‘safe’ mode,” said lead author Boxuan Zhong.
A ‘Significant’ AI Advancement
“The Incorporating computer vision into control software for wearable robotics is an exciting with new area of research,” said Helen Huang, a co-author of the paper. “We found that using both cameras worked well but required a great deal in computing power and may be cost-prohibitive. However, we also found that only using the camera mounted on the lower limb worked pretty well – particularly for near-term predictions, such as what the terrain would be like for the next step or two.”
A New Artificial Intelligence (AI) Training Model
“We found that the model can be appropriately transferred so that the system can operate with subjects from different populations,” said by Lobaton. “That means that the AI worked well even though it was trained by one group of people and used by somebody different.”