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The future of smart glasses is about to change drastically with the upcoming release of DigiLens ARGO range next year. These innovative smart glasses will be powered by Phantom Technology’s cutting-edge spatial AI assistant, CASSI. This partnership between DigiLens, based in Silicon Valley, and Phantom Technology, located at St John’s Innovation Centre, brings together the expertise of both companies to create a game-changing wearable device.

Phantom Technology, an AI start-up founded by a group of brilliant minds, has been working diligently over the years to develop advanced human interface technologies for AI wearables. Their breakthrough 3D imaging technology allows users to identify objects in their environment, enhancing their overall experience. With DigiLens incorporating Phantom’s patented optical platform into their consumer product, customers can expect a new era of smart glasses with unparalleled features.

CASSI, the novel spatial AI assistant designed by Phantom Technology, aims to boost productivity and awareness in enterprise settings. This innovative assistant combines computer vision algorithms with a large language model, enabling users to receive step-by-step instructions and assistance for various tasks. Imagine effortlessly locating any physical object or destination in the real world with 3D precision, using your voice to generate instructions, and seamlessly managing tasks using augmented reality.

They’re called vibrotactors and they vibrate to provide orientation cues.

A number of factors, such as the lack of gravity, changed sensory inputs, and the unique conditions of space travel, can cause disorientation in astronauts. This phenomenon is so severe that it can even be deadly to the space dwellers.


A new path for safer space travel

Usually, astronauts must undergo extensive training to guard against it. However, researchers have now released a new study that shows that wearable gadgets called vibrotactors that vibrate to provide orientation cues may greatly increase the effectiveness of this training providing a new path to making space travel safer and more comfortable for astronauts.

Batteries are regarded as crucial technologies in the battle against climate change, particularly for electric vehicles and storing energy from renewable sources. Anthro Energy’s novel flexible batteries are presently available to wearable manufacturers and could be employed in a variety of areas, including electric cars and laptops.

The innovative batteries score well in fire safety, thanks to new materials and design features that eliminate internal and external mechanical safety risks like explosions. Many of today’s batteries, such as lithium-ion batteries, contain a flammable liquid as an electrolyte.

Anthro Energy’s David Mackaniac and his team have created a flexible polymer electrolyte that is malleable like rubber. The new technology provides increased design flexibility for use across a range of devices, with adaptable shapes and sizes to suit specific applications.

The sky is no longer the limit—but taking flight is dangerous. In leaving the Earth’s surface, we lose many of the cues we need to orient ourselves, and that spatial disorientation can be deadly. Astronauts normally need intensive training to protect against it. But scientists have now found that wearable devices which vibrate to give orientation cues may boost the efficacy of this training significantly, making spaceflight slightly safer.

“Long-duration will cause many physiological and psychological stressors, which will make very susceptible to ,” said Dr. Vivekanand P. Vimal of Brandeis University in the United States, lead author of an article in Frontiers in Physiology on this topic. “When disoriented, an astronaut will no longer be able to rely on their own internal sensors, which they have depended on for their whole lives.”

The researchers used and a multi-axis rotation device to test their vibrotactors in simulated spaceflight, so the senses participants would normally rely on were useless. Could the vibrotactors correct the misleading cues the participants would receive from their vestibular systems, and could participants be trained to trust them?

Google’s new technique works by sending low-intensity ultrasonic probing signals via the speakers.

Researchers at Google have devised a technology that allows active noise-canceling (ANC) wearables to feature health-sensing applications.

The team utilized a technique called audio plethysmography (APG) in ANC wearables to monitor a user’s physiological data, such as heart rate and heart rate variability, without the need for additional sensors or sacrificing battery life.

Humane is set to reveal more about its mysterious new device on November 9th, but a new report from The Information says the gadget could have a high price.

The AI Pin, the new gadget / wearable device / projector / thing from the secretive startup Humane, might cost as much as $1,000 and may require a monthly subscription for data, according to The Information.

The mysterious device has been in development for years, but we got our first good look at it during co-founder Imran Chaudhri’s presentation at TED this year. In the presentation, he used then unnamed device to accept a phone call, get information about where to buy a gift, translate a sentence that is then spoken in an AI-made… More.


We might learn if that’s true on November 9th.

Link :- https://eng.unimelb.edu.au/ingenium/wearable-device-makes-me…f-a-finger


Researchers from the University of Melbourne and RMIT University have invented an experimental wearable device that generates power from a user’s bending finger and can create and store memories, in a promising step towards health monitoring and other technologies.

Multifunctional devices normally require several materials in layers, which involves the time-consuming challenge of stacking nanomaterials with high precision. This innovation features a single nanomaterial incorporated into a stretchable casing fitted to a person’s finger. The nanomaterial enables the device to produce power simply through the user bending their finger. The super-thin material also allows the device to perform memory tasks.

The team, led by RMIT University and the University of Melbourne, in collaboration with other Australian and international institutions, made the proof-of-concept device with the rust of a low-temperature liquid metal called bismuth, which is safe and well suited for wearable applications.

Forget the cloud.

Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.

With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics.

Forget the cloud. Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.

With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time and near-instant diagnostics.

To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy.