The colorful and customizable wearables can monitor heart rate, brain activity, and more. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Using a conductive, face-paint-like ink, researchers can now paint electrodes to monitor a wearer's heart, muscle or brain activity in style. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The next time you're scrolling your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has been a longstanding challenge in robotics and virtual reality. Now, MIT engineers have designed an ultrasound wristband that precisely tracks a wearer's hand movements in real-time. The wristband produces ultrasound images of the wrist's muscles, tendons, and ligaments as the hand moves, and is paired with an artificial intelligence algorithm that continuously translates the images into the corresponding positions of the five fingers and palm.

Meta Is Charging a Subscription for Smart Glasses Features. Now you'll need to subscribe for "expanded access" to the most advanced features. So you paid a few hundred bucks for a neat little gadget and think you're good to go. But it turns out you'll need to subscribe to a monthly plan to unlock its advanced features. That where AI-powered consumer electronics are increasingly heading, and Meta is the latest to prove it with its smart glasses .

Camera, wifi and design updates bring welcome upgrades to Ring's top model in wired or battery flavour The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. R ing's recent revamp of its popular video doorbells with a more modern design is led by the top-of-the-line Video Doorbell Pro 3, which gains much-needed upgrades with a 4K camera and better wifi plus new interesting AI features. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link.

After years of releasing smart glasses that bore the Ray-Ban or Oakley brand, Meta has finally made its own (although still in collaboration with Essilor Luxxotica). The company today unveiled a trio of AI Glasses -- the Fury, the Adventurer and the Meta Glasses by Kylie (labeled in some places as Starfire), and the first two of those styles start at $299. The variant that was co-designed with celebrity Kylie Jenner, will cost $399. At its launch event in New York City yesterday, Meta set us up with a pair of the new glasses and a companion phone, and let us roam around the venue and its demo areas somewhat freely. The company also had multiple units of the other styles around for us to pick up and try on as we liked, so I got a good sense of all the different options available.

The new Meta-branded glasses have the same camera, microphones, and chatbot as the Ray-Bans. They come in three styles, one of which was codesigned with Kylie Jenner. Smart glasses are like public transportation, according to Peter Bristol, Meta's vice president of industrial design. "People will use it when it's good enough." To reach "good enough," Meta is making its smart glasses more accessible, more customizable, and comfier to wear.

Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egocentric videos presents substantial challenges due to the dynamic nature of egocentric viewpoints, the intricate diversity of actions, and the complex variety of scenes encountered. Existing datasets are inadequate for addressing these challenges effectively. To bridge this gap, we present EgoVid-5M, the first high-quality dataset specifically curated for egocentric video generation. EgoVid-5M encompasses 5 million egocentric video clips and is enriched with detailed action annotations, including 5M high-level textual descriptions and 65K fine-grained kinematic control annotations. To ensure the integrity and usability of the dataset, we implement a sophisticated data cleaning pipeline designed to maintain frame consistency, action coherence, and motion smoothness under egocentric conditions. Furthermore, we introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals. The EgoVid-5M dataset, associated action annotations, and all data cleansing metadata will be released for the advancement of research in egocentric video generation.

Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these signals exhibit strong non-stationarity, with sharp peaks and abrupt changes that evolve continuously, making them difficult to represent using traditional time-domain or filtering methods. To address these issues, a novel waveletbased approach for physiological signal analysis is presented, aiming to capture multi-scale time-frequency features in various physiological signals. Leveraging this technique, two large-scale pretrained models specific to EMG and ECG are introduced for the first time, achieving superior performance and setting new baselines in downstream tasks. Additionally, a unified multi-modal framework is constructed by integrating pretrained EEG model, where each modality is guided through its dedicated branch and fused via learnable weighted fusion. This design effectively addresses challenges such as low signal-to-noise ratio, high inter-subject variability, and device mismatch, outperforming existing methods on multi-modal tasks. The proposed wavelet-based architecture lays a solid foundation for analysis of diverse physiological signals, while the multi-modal design points to nextgeneration physiological signal processing with potential impact on wearable health monitoring, clinical diagnostics, and broader biomedical applications.

We propose LiteReality, a novel pipeline that converts RGB-D scans of indoor environments into compact, realistic, and interactive 3D virtual replicas. LiteReality not only reconstructs scenes that visually resemble reality but also supports key features essential for graphics pipelines--such as object individuality, articulation, high-quality physically based rendering materials. At its core, LiteReality first performs scene understanding and parses the results into a coherent 3D layout and objects, with the help of a structured scene graph.

Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems.