Thanks to biomedical technology, cutting-edge upper-limb prostheses can lift a glass, make a fist, and enter a phone number with an index finger. However, things that work in the laboratory often encounter stumbling blocks when put to practice in daily life because of the vast diversity of the intentions of each individual person, their surroundings, and the things that can be found there, making a one size fits all solution all but impossible.
A team at the German university Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is investigating how intelligent prostheses can be improved and made more reliable. Their idea is that interactive artificial intelligence will help the prostheses recognize human intent better, register their surroundings, and continue to develop and improve over time.
The project, “AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics (IntelliMan),” is to receive 6 million euros (about $6.4 million) in funding from the European Union, with FAU receiving 467,000 euros (about $494,000).
“We are literally working at the interface between humans and machines,” said Claudio Castellini, PhD, a professor of medical robotics at FAU and head of the Assistive Intelligent Robotics lab that focuses on controlling assistive robotics for upper and lower limbs and functional electrostimulation.
“The technology behind prosthetics for upper limbs has come on in leaps and bounds over the past decades. Using surface electromyography, for example, skin electrodes at the remaining stump of the arm can detect the slightest muscle movements. These biosignals can be converted and transferred to the prosthetic limb as electrical impulses. The wearer controls their artificial hand themselves using the stump. Methods taken from pattern recognition and interactive machine learning also allow people to teach their prosthetic their own individual needs when making a gesture or a movement.”
The FAU researchers will concentrate on how to improve control real and virtual prosthetic upper limbs through intent detection by recording and analyzing human biosignals and designing algorithms for machine learning aimed at detecting the individual movement patterns of individuals.
Editor’s note: This story was adapted from materials provided by Friedrich-Alexander-Universität Erlangen-Nürnberg.
