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Meta Quest Quantitative Refinement
Client
Meta
Role
Lead UX Researcher
Date
December, 2022
Location
New York, NY & Chicago, IL
Project Type
Raw Data Collection & Benchmarking
Target Audience
B2C
Team
This team consisted of a Principal UX Researcher, a research scientist, a designer and a team of 4 research assistants
Success Metric
Increase accuracy scores/reduce false positives and negatives.
User Problem:
Despite availability, control-less hand tracking was underutilized, likely due to low accuracy.
Key Challenges:
This multi-state initiative required extensive cross-functional coordination beyond design and engineering, involving legal, accounting, and external research partner,Ipsos. Additionally, careful management of large datasets was required to ensure secure data transfer.
Methods:
1. Conducted a Chicago pilot (n=13) benchmarking study where users performed over 100 hand gestures captured via external and headset-mounted cameras.
2. Official data collection in New York (n=72). State-specific legal requirements necessitated slight protocol variations.
3. A quantitative survey assessed individual gesture feedback on a 5 point likert scale.
- Purposeful sampling targeted users with minimal hand tracking experience to simulate worst-case performance metrics. This required our research to span out to other states outside Washington.
Findings
Overall accuracy reached 85%—13% below controller benchmarks—indicating improvement needs. Gesture-level analysis identified specific refinement targets. Observed confounding factors included handedness, hand size, and resting hand position.
Impact:
By end of 2023, accuracy improved to 95%, achieving parity with controllers (98% accuracy for certain tasks). This research established frameworks for subsequent 2024 studies.
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