New research at the Technical University of Munich (TUM), Germany, showed that a better understanding of muscle activity patterns in the forearm supports a more intuitive and natural control of artificial limbs. This level of control requires a network of 128 sensors and artificial intelligence (AI).
The research team has shown that AI can enable patients to control advanced hand prostheses more intuitively by using the “synergy principle” and sensors on the forearm.
“[The synergy principle] is known from neuroscientific studies that repetitive patterns are observed in experimental sessions, both in kinematics and muscle activation,” said Cristina Piazza, PhD, a professor of rehabilitation and assistive robotics at TUM who is leading the research. “These patterns can be interpreted as the way in which the human brain copes with the complexity of the biological system. That means that the brain activates a pool of muscle cells, also in the forearm. When we use our hands to grasp an object, for example a ball, we move our fingers in a synchronized way and adapt to the shape of the object when contact occurs.”
The researchers are using this principle to design and control artificial hands by creating new learning algorithms necessary for intuitive movement. When controlling an artificial hand to grasp a pen, for example, multiple steps take place. First, the patient orients the artificial hand according to the grasping location, slowly moves the fingers together, and then grabs the pen. The goal is to make these movements increasingly fluid, so that it is hardly noticeable that numerous separate movements make up an overall process.
“With the help of machine learning, we can understand the variations among subjects and improve the control adaptability over time and the learning process,” said Patricia Capsi Morales, PhD, the senior scientist on the research team.
Experiments with the new approach indicate that conventional control methods could soon be empowered by more advanced strategies. To study what is happening at the level of the central nervous system, the researchers are working with two films: one for the inside and one for the outside of the forearm. Each contains up to 64 sensors to detect muscle activation. The method also estimates which electrical signals the spinal motor neurons have transmitted.
“The more sensors we use, the better we can record information from different muscle groups and find out which muscle activations are responsible for which hand movements,” said Piazza.
Current research concentrates on the movement of the wrist and the whole hand. It shows that eight out of ten people prefer the intuitive way of moving their wrists and hands. This is also the more efficient way. But two of ten learn to use the less intuitive way, becoming, in the end, even more precise.
“Our goal is to investigate the learning effect and find the right solution for each patient,” said Capsi Morales.
“This is a step in the right direction,” said Piazza, who emphasized that each system consists of individual mechanics and properties of the hand, special training with patients, interpretation and analysis, and machine learning.
There are still some challenges to address. The learning algorithm, which is based on the information from the sensors, has to be retrained every time the film slips or is removed. In addition, the sensors must be prepared with a gel to guarantee the necessary conductivity to record the signals from the muscles precisely. “We use signal processing techniques to filter out the noise and get usable signals,” said Capsi Morales. Every time a new patient wears the cuff with the many sensors over their forearm, the algorithm must first identify the activation patterns for each movement sequence to later detect the user’s intention and translate it into commands for the artificial hand.”
The study, “Exploring muscle synergies for performance enhancement and learning in myoelectric control maps,” was published as part of the IEEE International Conference on Rehabilitation Robotics 2023.
Editor’s note: This story was adapted from materials provided by Technical University of Munich.
