Direct nervous system link promises more natural leg prostheses
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Decoding motor intentions directly from within peripheral nerves may enable more natural and intuitive control of prosthetic limbs. In the study, researchers successfully interpreted signals from the sciatic nerve of above-knee amputees with high accuracy using AI-based spiking neural networks that mimic biological neural communication. The results represent a step towards neurally-controlled prosthetic legs, which could also restore the user’s sensation. The next step is to test the method on real prostheses. Illustration: Pietro Comaschi
view moreCredit: Pietro Comaschi
A research team led by researchers at Chalmers University of Technology in Sweden, has, for the first time, successfully decoded leg movements directly from the remaining nerves in people with above-knee amputations. Using novel implantable neurotechnology and an AI method based on the nervous system’s own “language”, the researchers could do what was previously impossible and interpret detailed movements – even the will to wiggle toes. This technology opens the way to future leg prostheses that feel and act more like a natural part of the body.
Helping amputees regain a functional and independent life through prostheses that the user can control has long been a major goal in biomedical research.
Currently, arm and hand prostheses are often controlled via the amputee’s remaining muscles, which are still activated by nerve signals from the brain. However, this requires that the relevant muscles are still intact, making muscle-based control impossible in cases of major amputation. For leg amputees, prosthetic legs typically rely on mechanical systems and built-in sensors which automatically adapt to walking without any active user control.
In a new study published in Nature Communications, the research team focused on making more direct use of the nerve signals that remain active post- amputation.
“When you tell your body to move, signals travel through the nerves to the muscles which carry out the action – even if the limb is no longer there,” says Giacomo Valle, assistant professor at Chalmers and one of the study’s senior authors. “This means you can find all the information needed within those nerves. The major challenge is extracting that information and understanding the neural code behind it – and that’s been the focus of our work.”
Reading signals directly from nerves
According to Valle, the ability to read and interpret movement signals directly from within nerves is key to developing future prostheses that are more responsive and intuitive.
“If an implant can be connected directly to the remaining nerves, instead of through residual muscles, you can use exactly the same natural signals used to move your limbs. It greatly increases the potential to create prostheses with natural control, sensory feedback* and unprecedented resolution,” he says.
However, extracting nerve signals directly from the remaining nerves of amputees is extremely challenging. Very few studies have been successful, and all have focused on the upper limbs – even though most people living with amputation have lost a leg. The research is complicated by the fact that the remaining post-amputation nerves produce weak signals that are difficult to capture reliably.
The research group has succeeded in meeting this challenge with a completely new approach focusing on leg amputees, in which the key role is played by a neurotechnological implant, combined with a new, AI-based algorithm.
The same type of neural implant (developed at the University of Freiburg) has been used in previous prosthetic research, but only to stimulate the remaining nerves and restore touch sensation. In this study, the researchers also succeeded in using the technology to read nerve signals in a precise and controlled manner.
In the next step, the researchers employed a new, AI-based technique to interpret the recorded nerve signals. The technique is based on so-called Spiking Neural Networks (SNNs), which differ from conventional AI systems (such as those used in for example ChatGPT or image recognition) by processing time-based signals known as “spikes,” rather than continuous numerical values.
According to Elisa Donati, professor at the University of Zurich and ETH Zürich and the other senior author of the study, these signals therefore mimic more closely how biological neurons communicate.
“Our study shows that decoding peripheral nerve** activity works best when it respects the language of the nervous system,” she says. “Peripheral nerves communicate through discrete electrical impulses – or spikes – and spiking neural networks are therefore naturally suited to processing this type of signal. By aligning our computational models more closely with biology, we can extract movement intent efficiently, using compact models and relatively limited data. This is an important step towards low-power, fully implantable systems for more natural control of prosthetic limbs.”
Decoding intended movements and restoring touch sensation
In the study, the researchers concentrated on above-knee amputations, carrying out tests on two participants. Four ultrathin neural implants – each about the size of a human hair and both flexible and pliable – were inserted into the tibial branch of the sciatic nerve, which plays a central role in driving leg movement and sensation. When participants were asked to attempt different movements with their “phantom leg,” the researchers recorded the outgoing nerve signals and decoded them with unprecedented high resolution using their AI-based algorithm.
“This is the first study to demonstrate that signals recorded directly from peripheral nerves can be used to accurately interpret intended leg movements in amputees,” says Valle. “With this approach, we were able to map specific nerve signals to specific movements and predict, with high accuracy, which movements the participants were attempting.”
The method provides the opportunity to interpret very specific leg movements for the knees, ankles and toes – even those that were previously impossible to decode.
“The study provides unique insight into how the nervous system transmits information. We’ve cracked the code of nerve communication and shown that it’s possible to interpret detailed leg movements, even in amputations where most of the leg is gone. It was amazing to see how electrodes placed high up in what remains of a leg could decode attempts to wiggle the toes,” Valle says.
According to the research group, another advantage is that the technology can be used for both motor control and restoring sensation, with a single implant. Until now, several different implants have been required for prostheses to be able to both “move” and “feel”.
“The system is bidirectional,” explains Valle. “Once electrodes are implanted inside the nerve, they can be used to communicate bidirectionally with the nervous system. So, for the first time, a single neurotechnology can provide both natural neural control and sensory feedback in the same implantable device.”
Next step: integrating the technology into a prosthetic leg
The study is a “proof of concept“, demonstrating that the technique is feasible. The next step is to test it on real prostheses. While the findings are particularly significant for the development of prosthetic legs, Valle believes the method could be extended to other types of prostheses in the future.
“I believe these results could significantly influence the field. The next step is to integrate and test the technology into a prosthetic leg that can be controlled directly and that can return natural sensation,” he says.
*Sensory feedback is the information that the brain constantly receives from the body’s sensory organs about the state of the body and the environment. It is the brain’s way of “feeling” what the body is doing and where it is, which allows us to interact with the world smoothly and safely.
** Peripheral nerves are nerve fibres located outside the brain and spinal cord.
More about the research:
The study Decoding phantom limb movements from intraneural recordings has been published in Nature Communications. The authors are Cecilia Rossi, Marko Bumbasirevic, Paul Čvančara, Thomas Stieglitz, Stanisa Raspopovic, Elisa Donati and Giacomo Valle. The researchers are affiliated with Chalmers University of Technology, Sweden, the University of Zurich and ETH Zürich, Switzerland, the University of Belgrade, Serbia, the University of Freiburg, Germany and the Medical University of Vienna, Austria.
Journal
Nature Communications
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Decoding phantom limb movements from intraneural recordings
Article Publication Date
8-Feb-2026
COI Statement
G.V. holds shares of “MYNERVA AG”, a start-up company dealing with the potential commercialization of noninvasive stimulating wearable for treating neuropathic pain. G.V. serves as a consultant for NeuroOne Medical Technologies Corporation (USA). The other authors do not have anything to disclose.
