The developers have created a fully automated system that learns in the process of normal
“In this system, the user and the system simultaneously learn from each other. This has the potential to improve the comfort and reliability of robotic prostheses,” Yeng says.
Research results published in the journalIEEE Transactions on Biomedical Engineering have been validated in a virtual environment. They were compared with currently existing systems. After successful tests, the research team tested the user interface and state-of-the-art prosthesis at Imperial College London. The developers asked an amputee to move the clothespins: tests like this are widely used by physical therapists to assess the function of the upper limbs.
A modern prosthetic arm controlled by muscle activation. Aalto University
System functionality has been studied through controlled and standardized tests, but user needs are best identified through qualitative longitudinal studies.
“For the future development, it is very important to continue cooperation with medical centers and users of prostheses,” says Yeng.
People who have had their upper limbs amputatedcan control a robotic prosthesis by contracting the remaining muscles. The connection where the prosthesis picks up the electrical signals produced by the muscle is known as the myoelectrical interface. The most advanced prostheses use machine learning algorithms to help interpret these user-generated signals. However, these bonds are often very sensitive to external factors, such as perspiration, and weaken over time. To solve this problem, the research community offers various algorithms that can better adapt to changing circumstances.
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