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Artificial muscles for tremor suppression


Source:Max Planck Institute for Intelligent Systems

FULL STORY


It is estimated that around 80 million people worldwide live with a tremor. For example, those who live with Parkinson’s disease. The involuntary periodic movements sometimes strongly affect how patients are able to perform daily activities, such as drinking from a glass or writing. Wearable soft robotic devices offer a potential solution to suppress such tremors. However, existing prototypes are not yet sophisticated enough to provide a real remedy.

Innovative Biorobotic Arm Could Revolutionize Tremor Treatment

Scientists at the Max Planck Institute for Intelligent Systems, in collaboration with the University of Tübingen and the University of Stuttgart, are aiming to transform the landscape of tremor management. Their latest innovation? A biorobotic arm equipped with advanced artificial muscles that mimic human tremors.

In a groundbreaking demonstration, this mechanical marvel—dubbed the “mechanical patient”—shows how it authentically simulates tremors. The team has recorded real tremors and programmed the biorobotic arm to replicate the unique movements of each individual. But here’s the game changer: once the tremor suppression feature is activated, the lightweight artificial muscles, made from cutting-edge electro-hydraulic actuators, spring into action, neutralizing the tremor to the point that it’s barely perceptible.

The ambitions for this biorobotic arm stretch beyond mere simulation. Researchers envision it as an essential testing platform for fellow scientists advancing assistive exoskeleton technology. By pairing this arm with biomechanical computer simulations, developers can swiftly validate the performance of their soft artificial muscles, potentially bypassing the often-prohibitive costs and lengthy timelines associated with clinical trials on actual patients, which are even legally restricted in some locations.

Moreover, the arm serves as a vital testing ground for the innovative HASEL technology developed by the Robotic Materials Department at MPI-IS. These artificial muscles have been meticulously refined over the years, aiming for a future where they become integral components of discreet wearable devices. Such devices could revolutionize how tremor patients perform everyday tasks, like enjoying a cup of coffee.

Alona Shagan Shomron, a postdoc at MPI-IS and the lead author of a recent study published in the journal “Device,” expresses her optimism: “We envision our artificial muscles as the core of a garment that offers discreet support, so those afflicted by tremors can navigate daily life without drawing attention.” Shagan adds that the HASEL-based muscles are not only powerful but also fast enough to tackle a broad spectrum of tremors affecting the wrist.

Daniel Häufle, a professor at the Hertie Institute for Clinical Brain Research, notes the arm’s capabilities: “By combining the mechanical patient with a biomechanical model, we can evaluate whether any tested artificial muscles can effectively suppress even the strongest tremors, tailoring a potential wearable device to respond accurately to individual needs.”

Syn Schmitt, Professor for Computational Biophysics and Biorobotics at the University of Stuttgart, highlights the importance of early-stage testing: “The mechanical patient offers us a unique opportunity to explore new technologies well before the clinical testing phase, eliminating the barriers of high costs and extensive timelines that often stifle promising innovations.” With this groundbreaking approach, the team hopes to unlock the full potential of assistive technology for those living with tremors.

Our mechanical patient is the solution which allows us to test the potential very early in the development.”

“Robotics has great potential for healthcare applications. This successful project highlights the key role that soft robotic systems, based on flexible and deformable materials, will play,” Christoph Keplinger, the Director of the Robotic Materials Department at MPI-IS, concludes.

Story Source:

Materials provided by Max Planck Institute for Intelligent SystemsNote: Content may be edited for style and length.

This work was supported by grants.


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