Human Factors
Anthropometric data is the foundation of our augmented reality expertise.
Our deep research into facial geometry, fit, comfort, and user experience enables us to design AR devices that work with, not against, the human body.
Designed Around People
There’s no such thing as an “average face.” Magic Leap’s biometric research spans thousands of individuals across age, gender, and ethnicity to capture nuanced variation in head shape, nose shape, eye position, and more. Only by starting with vast and detailed data can we design for everyone.
Submillimeter Precision
Our 3D scans capture hundreds of thousands of vertex points in milliseconds, creating a high-resolution dataset for accurate modeling and fit validation.
Diverse Sampling
Data collection includes regional, age-based, and ethnically diverse sampling to reflect the needs of a global user base.
Comprehensive Anthropometry Data
Our expert anthropometrists supplement our 3D head and hand scan data with physical landmarking of anatomical features and traditional 1D measurements for wider database comparison.
Human-Centered Testing
Extensive user testing enables thorough, representative understanding of user interaction, visual and audio performance, comfort, and how users wear devices during daily activities.
The Science of Comfort
Our Human Factors team measures everything from nose bridge angles to temple arm pressure to quantify what makes a device feel comfortable. Magic Leap's fit testing protocols track how devices perform over hours of wear and across everyday activities. This data informs every millimeter of our designs.
Designing for Facial Diversity
Using the latest anthropometry methods, we identify diverse facial geometries to simulate fit and guide material and structural design for broad user comfort.
Foundational Comfort Understanding
Research into pressure, temperature, and weight distribution helps our teams design for long-term comfort and wearability during extended use.
Eye-Alignment Modeling
Extensive eye-position simulation allows us to optimally position displays for best display brightness, visual comfort, and experience.
Predictive Fit
Devices are tested on hundreds of diverse users for comfort, fit, stability, and visual alignment, building a fit database that informs improved fit simulation.
True Fit Data
Simulation is just the start of the design process. Which is why we gather empirical fit data from actual users to quantify how devices sit, feel, and move with the user. That data bridges the gap between virtual models and comfort-optimized physical devices users can wear all day.
Authentic Fit Feedback
Structured fit tests on diverse users evaluate long-term comfort, stability during movement, and display alignment, providing real-world data to improve comfort and reliability.
Usability and Experience Mapping
We observe and analyze user behaviors such as how they pick devices up, put them on, adjust them, and more. Deep analysis of these behaviors inform hardware design decisions.
Slippage Testing
We monitor everyday movement, like walking stairs, navigating crowded spaces, and interacting with children and pets, to reduce shift, bounce, or discomfort during daily wear.
Wear Duration Insights
We collect comfort data at multiple intervals across lab tests and conduct at-home diary studies to ensure all-day wear readiness.
Built for Impact
For over a decade, Magic Leap has built one of the world’s most comprehensive biometric datasets for augmented reality design. These datasets are more than mere research, they offer risk reduction, insight generation, and a head start on every design.
Informed Design Guidance
We apply structured data models to guide engineering decisions and validate designs across partner programs.
Longitudinal Insights
Years of data and testing show how comfort, fatigue, and usage evolve across designs and over time.
Predictive Tools
Modeling tools built on large datasets help forecast how designs perform across varied users.
Faster, Smarter Development
Extensive first-party research accelerates development by reducing trial and error in material design, fit, and ergonomics.