More than 1.6 million Americans have suffered the loss of a limb. Yet, despite advancements in technology, prosthetics still fall short. Researchers at FAU want to change that.
Current prosthetic hands have five individual digits, yet only one grasp function can be controlled at a time. That means simple tasks like the ability to use a screwdriver or can opener — let alone more complex tasks — are largely impossible, said Erik Engeberg, Ph.D., professor in FAU College of Engineering and Computer Science and member of the FAU Stiles- Nicholson Brain Institute.
“Anything exceeding basic functionality remains elusive for prosthetic hands even though they are mechanically capable of such feats,” said Engeberg who recently earned a four-year, $1.2 million grant from the National Science Foundation to work with amputees to learn advanced control over more sophisticated prosthetic hands, with an automated training regimen that can be used at home.
The project involves a novel bimodal skin sensor that combines machine learning motor intention classification algorithms and reinforcement learning. Clinicians will interact with 10 study participants over the course of one year for muscle training using a smartphone.
“Automating this aspect of health care with remote learning functionality can help disabled people access treatment more quickly, more conveniently and at a lower cost,” Engeberg said.
Currently, wrist flexor muscles are used to close the prosthetic hand while the extensor muscles open the prosthetic hand. In an unnatural manner, the user must then toggle between different grasp types. Although there are numerous options for dexterous wearable co-robot assistants, such as prosthetic hands, the dexterity of these devices is rapidly outpacing the ability for people to intuitively control them.
One main source of this problem stems from the inability to reliably interpret the intentions of the human operator over the course of months and years. Another problem is that the science behind customizable training programs to empower disabled people to harness the full potential of prosthetic hands has not been deeply explored.
“The uniquely holistic approach developed by professor Engeberg and his colleagues to transform the state-of-the-art for dexterous control of prosthetic hands could break through previously insurmountable barriers.”
– Stella Batalama, Ph.D., dean,
FAU College of Engineering and Computer Science
“This non-intuitive functionality is why many amputees reject using artificial limbs, which is unfortunate because of the negative collateral effects at work and for pleasure, which drastically impact their quality of life,” Engeberg said. “The current clinical state- of-the-art has a minimal level of dexterous controllability; overcoming this problem is the goal of our research.”
For each of the 10 amputees recruited for the study, the researchers will 3D scan their residual limbs to fabricate form-fitting prosthetic sockets that are adaptable to anticipated changes in residual limb musculature over the course of the program. They will develop a novel bimodal robotic skin to sense biocontrol signals in the residual limbs that will be integrated within the customized prosthetic sockets to overcome limitations with current sensing technology. Researchers will train the classification algorithms using data gathered from in-home experiments performed over the course of one year.
The technology also will provide the research team with the ability to monitor the patients’ usage data from remote locations, which has broad applications to connect disabled people located around the world with specially trained clinical teams.
“Losing an upper limb has a devastating impact on the ability to perform common daily activities,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “The uniquely holistic approach developed by professor Engeberg and his colleagues to transform the state-of-the-art for dexterous control of prosthetic hands could break through previously insurmountable barriers.”
In addition, research from this grant will be used to create learning experiences for high school students from low-income households to help educate the next generation of engineers and scientists.