Lifeboat Foundation

Recent technological advancements have opened invaluable opportunities for assisting people who are experiencing impairments or disabilities. For instance, they have enabled the creation of tools to support physical rehabilitation, to practice social skills, and to provide daily assistance with specific tasks.

Researchers at Meta AI recently developed a promising and non-invasive method to decode speech from a person’s brain activity, which could allow people who are unable to speak to relay their thoughts via a computer interface. Their proposed method, presented in Nature Machine Intelligence, merges the use of an imaging technique and machine learning.

“After a stroke, or a brain disease, many patients lose their ability to speak,” Jean Remi King, Research Scientist at Meta, told Medical Xpress. “In the past couple of years, major progress has been achieved to develop a neural prosthesis: a device, typically implanted on the motor cortex of the patients, which can be used, through AI, to control a computer interface. This possibility, however, still requires brain surgery, and is thus not without risks.”

Nolej’s cofounder, Bodo Hoenen, used AI to help design an exoskeleton for his paralysed daughter, Lorelei.

A speech prosthetic developed by a collaborative team of Duke neuroscientists, neurosurgeons, and engineers can translate a person’s brain signals into what they’re trying to say.

Appearing Nov. 6 in the journal Nature Communications, the new technology might one day help people unable to talk due to neurological disorders regain the ability to communicate through a brain-computer interface.

“There are many patients who suffer from debilitating motor disorders, like ALS (amyotrophic lateral sclerosis) or locked-in syndrome, that can impair their ability to speak,” said Gregory Cogan, Ph.D., a professor of neurology at Duke University’s School of Medicine and one of the lead researchers involved in the project. “But the current tools available to allow them to communicate are generally very slow and cumbersome.”

The brain-computer interface (BCI) space continues to rise in notoriety, and a number of players are throwing their hats in the ring.

Such technologies could enable users to control a computer with their brain, or even go beyond that. Countless immobile people someday could control a mouse cursor, keyboard, mobile device/tablet, wheelchair or prosthetic device by only thinking.

Big names have already established their presence in the space. Elon Musk’s Neuralink continues to make headway, while Bill Gates-and Jeff Bezos-backed Synchron has an innovative catheter-delivered implant. Blackrock Neurotech, which has a next-generation BCI, has been implanting its Utah Array in patients since 2004.

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A speech prosthetic developed by a collaborative team of Duke neuroscientists, neurosurgeons, and engineers can translate a person’s brain signals into what they’re trying to say.

She gets a new cybernetic arm.

Watch this teen‘s wholesome reaction to getting her very first bionic arm.

During the company’s second quarter earnings call in July, Musk had said that combining the two could provide a cyber body that is incredibly capable.

An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Previous approaches to create artificial tactile sensors for sensing physical stimuli, such as pressure, have been limited in their ability to identify real-world objects upon touch, or they rely on multiple sensors. Creating a real-time artificial sensory system with high spatiotemporal resolution and sensitivity has been challenging.

Chuan Fei Guo and colleagues present a flexible slip sensor that mimics the features of a human fingerprint to enable the system to recognize small features on surface textures when touching or sliding the sensor across the surface. The authors integrated the sensor onto a prosthetic human hand and added machine learning to the system.