âMy work has focused on developing technology that translates electrical signals in human muscle into signals that control powered prosthetic limbs â such as decoding muscle signals to tell a prosthetic leg that it needs to walk forward or step up onto a staircase,â says Dr. Helen Huang, senior author of a paper on the work and an associate professor in the joint biomedical engineering program at North Carolina State University and the University of North Carolina at Chapel Hill.
âBut sometimes this âdecodingâ technology makes mistakes, such as thinking someone wants to climb a step when he doesnât,â says Fan Zhang, lead author of the paper and a Ph.D. student in the joint biomedical engineering program. âThis is a problem, because we donât want to put users at risk of stumbling or falling.â
Huangâs team set out to understand exactly what happens to users of powered prosthetic legs when thereâs an error in the decoding technology.
âWe not only want to improve the decoding accuracy, but determine which errors are important and which have little or no impact on users,â Huang says. âUnderstanding the problem is an important step in finding ways to make these prostheses more reliable.â
To address the issue, the researchers had study subjects use a customized prosthetic device that was programmed to make errors. This was done in a lab setting that allowed Huangâs team to monitor each userâs balance and biomechanics. Users were also asked how stable they felt during each trial.
The researchers found that some errors were so insignificant that users didnât even notice them â particularly errors that were short in duration or that occurred when a userâs weight was not being applied to the prosthetic leg.
But errors that lasted longer, or that occurred when a userâs weight was on the prosthetic limb, were more noticeable. The researchers also determined that critical, or especially noticeable, errors were also characterized by a large âmechanical work change,â meaning the prosthetic limb thought it had to do significantly more or less work than the user intended.
âOne of the things weâll be doing as we move forward with this work is seek ways to limit that mechanical work change,â Huang says.
âAny system that involves a human interface will have occasional errors,â Huang notes. âBut we think we can find ways to make those errors effectively insignificant.â
The paper, â,â is published in early view online in the journal IEEE Transactions on Neural Systems and Rehabilitation Engineering. The paper was co-authored by Ming Liu, a laboratory manager in the joint biomedical engineering program. The work was supported by the National Science Foundation under grants number 1406750 and 1361549, by the Department of Defense under grant number W81XWH-09-2-0020, and by the National Institute on Disability and Rehabilitation Research under grant number H133G130308.
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