Lifeboat Foundation

Imagine a patient controlling the movement of his or her prosthetic limb simply by thinking of commands. It may sound like science fiction but will soon become reality thanks to the EU-funded DeTOP project. A consortium of engineers, neuroscientists and clinicians has made great strides in further developing the technology behind more natural and functional prostheses.

The team uses an osseointegrated human-machine gateway (OHMG) to develop a physical link between a person and a robotic prosthesis. A patient in Sweden was the first recipient of titanium implants with the OHMG system. The OHMG is directly fitted to bones in the user’s arms, from which electrodes to nerves and muscle extract signals to control a robotic hand and provide tactile sensations. According to a news item by “News Medical,” the patient will begin using a training prosthesis in the next few months before being fitted with the new artificial hand developed by DeTOP partners. This will help the team evaluate the entire system, including the implanted interface, electronics, as well as wrist and hand functions. Motor coordination and grip strength will also be assessed during the tests.

Research on robotic prostheses is coming along in leaps and bounds, but one hurdle is proving quite tricky to overcome: a sense of touch. Among other things, this sense helps us control our grip strength — which is vitally important when it comes to having fine motor control for handling delicate objects.

Enter a new upgrade for the LUKE Arm — named for Luke Skywalker, the Star Wars hero with a robotic hand. Prototype versions of this robotic prosthesis can be linked up to the wearer’s nerves.

Continue reading “This Robotic Arm Inspired by Luke Skywalker Has Allowed an Amputee to Feel Again” »

Researchers from RMIT University have drawn inspiration from optogenetics, an emerging tool in biotechnology, to develop a device that replicates the way the brain stores and loses information. Optogenetics allows scientists to delve into the body’s electrical system with incredible precision, using light to manipulate neurons so that they can be turned on or off.

The new chip is based on an ultra-thin material that changes electrical resistance in response to different wavelengths of light, enabling it to mimic the way neurons work to store and delete information in the brain. Research team leader Dr. Sumeet Walia said the technology has applications in artificial intelligence (AI) technology that can harness the brain’s full sophisticated functionality.

“Our optogenetically-inspired chip imitates the fundamental biology of nature’s best computer—the human brain,” Walia said. “Being able to store, delete and process information is critical for computing, and the brain does this extremely efficiently. We’re able to simulate the brain’s neural approach simply by shining different colors onto our chip. This technology takes us further on the path towards fast, efficient and secure light-based computing. It also brings us an important step closer to the realization of a bionic brain—a brain-on-a-chip that can learn from its environment just like humans do.”

This exoskeleton is basically a chairless chair.

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.

Real Cyborg.

To develop a new artificial muscle for robots MIT researchers are taking inspiration from an unlikely source – the cucumber. It’s not the fruit of the plant that’s good for sandwiches and salads that the engineers are interested in, but the tightly coiled tendrils that wrap themselves around solid objects to support the growing plant by corkscrewing and pulling with surprising force.

Machine enhanced humans — or cyborgs as they are known in science fiction — could be one step closer to becoming a reality, thanks to new research Lieber Group at Harvard University, as well as scientists from University of Surrey and Yonsei University.

Researchers have conquered the monumental task of manufacturing scalable nanoprobe arrays small enough to record the inner workings of human cardiac cells and primary neurons.

The ability to read electrical activities from cells is the foundation of many biomedical procedures, such as brain activity mapping and neural prosthetics. Developing new tools for intracellular electrophysiology (the electric current running within cells) that push the limits of what is physically possible (spatiotemporal resolution) while reducing invasiveness could provide a deeper understanding of electrogenic cells and their networks in tissues, as well as new directions for human-machine interfaces.

A 3D-printed prosthetic hand controlled using a new AI-based approach could significantly lower the cost of bionic limbs for amputees.

Real need: There are approximately 540,000 upper-limb amputees in the United States, but sophisticated “myoelectric” prosthetics, controlled by muscle contractions, are still very expensive. Such devices cost between $25,000 and $75,000 (not including maintenance and repair), and they can be difficult to use because it is hard for software to distinguish between different muscle flexes.

Handy invention: Researchers in Japan came up with a cheaper, smarter myoelectric device. Their five-fingered, 3D-printed hand is controlled using a neural network trained to recognize combined signals—or, as they call them, “muscle synergies.” Details of the bionic hand are published today in the journal Science Robotics.