Hapware's Aleye is trying to unlock new levels of communication for people who are blind. The Aleye wristband (left) is a bit chunkier than a standard Apple Watch. It's only been a few months since Meta announced that it would open its smart glasses platform to third-party developers . But one startup at CES is already showing off how the glasses can help power an intriguing set of accessibility features. Hapware has created Aleye, a haptic wristband that, when paired with Ray-Ban Meta smart glasses, can help people understand the facial expressions and other nonverbal cues of the people they are talking to.

The Public Says'No Thanks' The maker of the eCoffee Energyband says it electrically stimulates your nerves to keep you alert. Researchers are skeptical, and critics see it as a way for China's bosses to keep workers productive. Forget coffee, you can now stay alert by strapping on a wristband that lightly zaps you awake. That's what eCoffee Energyband, a Chinese gadget that sells for just over $100, is claiming to do. First released in late 2023, the product is a lightweight wearable with two electrode pads that sit against the inner wrist.

Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone.

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. County gets a new tool to find and save vulnerable people with cognitive disabilities Jordan Wall, 27, of Chatsworth, -- an athlete, actor and global messenger for the Special Olympics -- wears her new GPS watch from the group L.A. Found on Oct. 15, 2025. The county program L.A. Found offers free tracking devices to residents with cognitive disabilities who are at risk of wandering away from home. Since launching seven years ago, more than 1,800 people have received devices through the program, with 29 successfully located after going missing. Janet Rivera cares for both her 79-year-old mother, who has dementia, and her 25-year-old son, who has a genetic condition called Fragile X syndrome.

Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone.

Mark Zuckerberg wears artificial intelligence-powered glasses as he speaks at the Meta's Connect developers conference on 17 September in Menlo Park, California. Mark Zuckerberg wears artificial intelligence-powered glasses as he speaks at the Meta's Connect developers conference on 17 September in Menlo Park, California. As humanity inches closer to an AI apocalypse, a sliver of hope remains: the robots might not work. Such was the case last week, as Mark Zuckerberg attempted to demonstrate his company's new AI-enabled smart glasses. "I don't know what to tell you guys," Zuckerberg told a crowd of Meta enthusiasts as he tried, and failed, for roughly the fourth time to hold a video call with his colleague via the glasses.

Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Early, accurate detection is essential to prevent cardiovascular complications, yet current monitoring methods are either invasive or rely on subjective symptom reporting, limiting applicability in daily file. This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors. Data were collected from 27 individuals with chronic SCI during urodynamic studies, including electrocardiography (ECG), photoplethysmography (PPG), bioimpedance (BioZ), temperature, respiratory rate (RR), and heart rate (HR), across three commercial devices. Objective AD labels were derived from synchronized cuff-based BP measurements. Following signal preprocessing and feature extraction, BorutaSHAP was used for robust feature selection, and SHAP values for explainability. We trained modality- and device-specific weak learners and aggregated them using a stacked ensemble meta-model. Cross-validation was stratified by participants to ensure generalizability. HR- and ECG-derived features were identified as the most informative, particularly those capturing rhythm morphology and variability. The Nearest Centroid ensemble yielded the highest performance (Macro F1 = 0.77+/-0.03), significantly outperforming baseline models. Among modalities, HR achieved the highest area under the curve (AUC = 0.93), followed by ECG (0.88) and PPG (0.86). RR and temperature features contributed less to overall accuracy, consistent with missing data and low specificity. The model proved robust to sensor dropout and aligned well with clinical AD events. These results represent an important step toward personalized, real-time monitoring for individuals with SCI.

The glasses' sensor technology opens up new possibilities for research and development in augmented reality applications. Meta's new gesture control wristband might just be the most seamless way to control a computer yet. And no, it doesn't require surgery, a camera, or even a touchscreen. All it needs is your wrist. This futuristic device uses electrical signals from your muscles to understand what your hand wants to do, even if it never actually moves.

This study aims to develop a deep learning system for an accessibility device for the deaf or hearing impaired. The device will accurately localize and identify sound sources in real time. This study will fill an important gap in current research by leveraging machine learning techniques to target the underprivileged community. The system includes three main components. 1. JerryNet: A custom designed CNN architecture that determines the direction of arrival (DoA) for nine possible directions. 2. Audio Classification: This model is based on fine-tuning the Contrastive Language-Audio Pretraining (CLAP) model to identify the exact sound classes only based on audio. 3. Multimodal integration model: This is an accurate sound localization model that combines audio, visual, and text data to locate the exact sound sources in the images. The part consists of two modules, one object detection using Yolov9 to generate all the bounding boxes of the objects, and an audio visual localization model to identify the optimal bounding box using complete Intersection over Union (CIoU). The hardware consists of a four-microphone rectangular formation and a camera mounted on glasses with a wristband for displaying necessary information like direction. On a custom collected data set, JerryNet achieved a precision of 91. 1% for the sound direction, outperforming all the baseline models. The CLAP model achieved 98.5% and 95% accuracy on custom and AudioSet datasets, respectively. The audio-visual localization model within component 3 yielded a cIoU of 0.892 and an AUC of 0.658, surpassing other similar models. There are many future potentials to this study, paving the way to creating a new generation of accessibility devices.

Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality of life for patients and caregivers due to the symptoms arising from it. Agitation and aggression (AA) are some of the symptoms of people with severe dementia (PwD) in long-term care or hospitals. AA not only causes discomfort but also puts the patients or others at potential risk. Existing monitoring solutions utilizing different wearable sensors integrated with Artificial Intelligence (AI) offer a way to detect AA early enough for timely and adequate medical intervention. However, most studies are limited by the availability of accurately labeled datasets, which significantly affects the efficacy of such solutions in real-world scenarios. This study presents a novel comprehensive approach to detect AA in PwD using physiological data from the Empatica E4 wristbands. The research creates a diverse dataset, consisting of three distinct datasets gathered from 14 participants across multiple hospitals in Canada. These datasets have not been extensively explored due to their limited labeling. We propose a novel approach employing self-training and a variational autoencoder (VAE) to detect AA in PwD effectively. The proposed approach aims to learn the representation of the features extracted using the VAE and then uses a semi-supervised block to generate labels, classify events, and detect AA. We demonstrate that combining Self-Training and Variational Autoencoder mechanism significantly improves model performance in classifying AA in PwD. Among the tested techniques, the XGBoost classifier achieved the highest accuracy of 90.16\%. By effectively addressing the challenge of limited labeled data, the proposed system not only learns new labels but also proves its superiority in detecting AA.