meta·Jun 23, 2026·3 min readCurated summary

How Meta Engineered Ultra-Narrow Batteries for AI Glasses

Smart glasses require batteries that fit inside extremely narrow temple arms while powering cameras, speakers, AI processing, and displays. Meta addressed this limitation by developing ultra-narrow steel-can cells, including batteries as thin as 7 mm, with redesigned electrode structures and tighter manufacturing tolerances. The approach increased capacity and peak-power performance while enabling different configurations across successive generations of Meta’s wearables. ## Why Traditional Pouch Cells Fall Short - Common in phones and laptops, pouch cells are difficult to shrink and reshape. - Folding, manufacturing tolerances, and wasted internal volume are especially costly in glasses. - Small pouch cells may also struggle to deliver peak power when several features operate simultaneously, such as recording video while an AI model processes a request. - Smart glasses instead need rigid, precisely shaped batteries that use nearly every available micron. ## Designing Ultra-Narrow Steel-Can Cells - Steel-can batteries are established in products such as watches and power tools, but Meta needed unprecedented widths down to 7 mm. - Engineers replaced the conventional wound “jelly roll” electrode with die-cut, stacked layers. - This architecture reduces impedance, helping prevent power drops or brownouts during simultaneous high-demand tasks. - Steel cans maintain their shape to approximately 100 microns, preserving usable space and improving energy density in narrow cells. ## Increasing Capacity Through System Design - The second-generation Ray-Ban Meta battery increased from 160 mAh to 210 mAh, about a 30% capacity increase. - The glasses nevertheless claimed roughly twice the runtime because of broader hardware and software improvements. - Gains came from better power management, tighter firmware control, and a form factor that accommodated a larger cell. - This demonstrates that battery life depends on the entire system, not chemistry alone. ## Managing Multiple Batteries and Higher Power Demands - Oakley Meta Vanguards use one battery in each temple arm. - Although the cells are symmetrical, the electrical loads are not evenly distributed. - Engineers had to address cross-charging risks and coordinate battery sequencing during startup and shutdown. - Meta Ray-Ban Display glasses created a sustained power demand because the display draws energy continuously rather than in short bursts. - They use a 248 mAh steel-can cell, the largest in Meta’s lineup. ## Scaling the Technology - Meta’s narrow steel-can design could support other wearable form factors beyond smart glasses. - The company is working to scale production across multiple vendors and build a more resilient supply chain. - Developing these cells required coordination among electrical, mechanical, firmware, manufacturing, and global collaboration teams. Meta’s steel-can battery technology shows how wearable battery improvements come from rethinking both cell construction and overall device engineering. For future compact wearables, precisely shaped, low-impedance cells combined with system-level power optimization offer a practical path to longer runtime and more demanding features.

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WearablesSmart GlassesBattery TechnologySteel Can Cells+3

google·Jan 14, 2026Original article

Unlocking health insights: Estimating advanced walking metrics with smartwatches (opens in new tab)

Google researchers have validated that smartwatches are a highly reliable and accurate platform for estimating complex spatio-temporal gait metrics, rivaling the performance of smartphone-based methods. By utilizing a multi-head deep learning model, the study demonstrates that wrist-worn devices can provide continuous, lab-grade health insights into a user's walking speed, step length, and balance without requiring the specific pocket placement or specialized laboratory equipment previously necessary for such data. ## Multi-Head Deep Learning for Wrist-Based Sensors * The researchers developed a temporal convolutional network (TCN) architecture designed to process raw inertial measurement unit (IMU) data, specifically 3-axis accelerometer and gyroscope signals sampled at 50 Hz. * Unlike traditional models that only track temporal events and are prone to integration drift, this multi-head approach directly estimates both unilateral and bilateral metrics simultaneously. * The model architecture extracts embeddings from the IMU signals and concatenates them with user height (a demographic scalar input) to improve the precision of spatial predictions. * The system estimates a comprehensive suite of metrics, including gait speed, double support time (the proportion of time both feet are on the ground), step length, swing time, and stance time. ## Large-Scale Validation and Study Protocol * To ensure rigorous results, the study involved a diverse cohort of 246 participants across two international sites, generating approximately 70,000 walking segments. * Ground truth measurements were captured using a professional-grade Zeno Gait Walkway system to provide high-precision reference data for comparison. * The study protocol included various walking conditions to test the model's versatility: a self-paced six-minute walk test (6MWT), fast-paced walking, and induced physical asymmetry created by wearing hinged knee braces at specific angles. * Researchers employed a five-fold cross-validation strategy, ensuring that all data from a single participant remained within a single split to prevent data leakage and ensure the model generalizes to new users. ## Clinical Validity and Comparative Performance * Smartwatch estimates demonstrated strong validity and excellent reliability, with Pearson correlation coefficients (r) and intraclass correlation coefficients (ICC) exceeding 0.80 for most metrics. * Performance comparisons showed non-significant differences in Mean Absolute Percentage Error (MAPE) between the Pixel Watch and Pixel phone, establishing the smartwatch as a viable alternative to smartphone-based tracking. * While double support time showed slightly lower but acceptable reliability (ICC 0.56–0.60), other metrics like step length and gait speed proved highly consistent across different walking speeds and styles. * The model’s success suggests that smartwatches can effectively bridge the gap in gait analysis, providing a more practical and consistent platform for continuous health tracking than handheld devices. This research establishes smartwatches as a powerful tool for longitudinal health monitoring, enabling the detection of neurological or musculoskeletal changes through passive, continuous gait analysis in everyday environments.

WearablesAIDeep LearningHealth Tech+5