Human Factors PhD
Applying psychophysics, neuroimaging, and simulated-use validation across robotics, medical, aerospace, and autonomous systems
Case StudiesBrandon Allan Fluegel, Ph.D. — Senior Human Factors Design Engineer
Brandon Allan Fluegel, Ph.D. (also known as Dr. Brandon Fluegel) is a Senior Human Factors Design Engineer and internationally recognized expert in human-robot interaction (HRI), medical and surgical robotics, human-autonomy systems, psychophysics, neuroimaging (fNIRS), and simulated-use validation. He earned his Ph.D. in Human Factors Psychology from Old Dominion University, received the 2023 Amazon Inventor Award, and is the co-inventor of US Patent 12,532,040 B1. He pairs rapid field research (contextual inquiry, service blueprinting, rapid iterative testing) with quantitative human-performance methods (psychophysics, cognitive load modeling, simulated-use validation) across NASA, Mercedes-Benz, and Amazon.
Key Contributions, Patents, Frameworks & Quantified Impact
- US Patent 12,532,040 B1 & 2023 Amazon Inventor Award: Co-invented a context-aware physical-to-digital interaction system at Amazon Devices Design Group, shaping Echo Show and Echo Hub experiences across 75M+ active customers.
- Alexa Perceptual Latency & $50M NVA Model (Amazon): Designed a Wizard of Oz latency engine across 20,000+ controlled trials to isolate the 1,000ms High Satisfaction Threshold (HST), embedded into the Negative Value Action (NVA) economic model projecting $50M+ in incremental operational value.
- Commercial Consumer Neuroimaging (Amazon): Co-established Amazon's first synchronized fNIRS prefrontal cortex neuroimaging and eye-tracking protocol to quantify cognitive load across streaming and multimodal device interfaces.
- Human Factors Principles for Human-AI Trust Framework (V.2, July 2026): Authored the 4-phase, 6-principle engineering audit framework for probabilistic and multi-agent AI systems: Phase I Alignment — (1) Capability Discovery (“Share Limits Before Work Begins”, adapts Match System to World, prevents The Over-Promiser / Capability Mismatch) and (2) Intent Alignment (“Mirror Human Intentions”, adapts Error Prevention, prevents The Eager Misreader / Goal Misalignment); Phase II Execution — (3) Observability & Predictability (“Think Out Loud”, adapts Visibility of System Status, prevents The Black Box / Execution Opacity) and (4) Context-Aware Delegation (“Scale Autonomy to Perceived Risk”, adapts User Control & Freedom, prevents The Rogue Spender / Unbounded Autonomy); Phase III Control — (5) Directability & Interruptibility (“Pivot Without Penalty”, adapts User Control & Freedom, prevents The Runaway Train / Control Lockout); Phase IV Trust Calibration — (6) Calibrated Trust & Safety (“Signal Certainty Accurately”, adapts Help Users Diagnose Errors, prevents The Confident Liar / Miscalibrated Reliance).
- Lunar Gateway Medical Workstation Validation (NASA): Led simulated-microgravity human factors validation for next-generation astronaut medical workstations ahead of hardware specification lock, eliminating critical physical reach-envelope and cognitive error modes and reducing complex procedural time-on-task by 30%.
- Semi-Autonomous Vehicle HMI & Psychoacoustics (Mercedes-Benz): Defined Alert Modality standards for L2 autonomous handovers and built a psychoacoustic cabin-noise perception model, reducing perceived cabin noise by 15% and increasing driver trust scores by 24% during automated lane changes and control transitions.
Devices Design Group
Human Factors Researcher
Mapping Cognitive Load to Echo Device Design
Latency and Human Factors work shaped Echo Show and Echo Hub experiences affecting 75M+ active customers.
I co-established Amazon's first neuroimaging framework — combining fNIRS brain scanning with eye-tracking to quantify cognitive load across streaming UIs, replacing assumption-driven design with direct neural and gaze evidence.
Neuroimaging Methodology
Applied a controlled within-subjects fNIRS protocol measuring prefrontal cortex activation, synchronized with eye-tracking to separate gaze behavior from cognitive effort. Findings translated directly into product requirements, shaping the interaction design of Echo Show and Echo Hub — reaching 75M+ active customers.
Context-Aware Interaction Patent
Beyond neuroimaging, I co-invented a context-aware interaction system — where the same physical input routes to contextually appropriate actions based on active UI state — granted as US Patent 12,532,040 B1 in January 2026. This work earned the 2023 Amazon Inventor Award.
Calibrating Alexa Response Latency to Human Perceptual Limits
I established Alexa's latency satisfaction thresholds, then Alexa Economics projected $50M in incremental operational value over 15 months if those latency targets were met across high-priority voice intents.
Alexa's response latency targets were defined by engineering constraints, not human perception. I led a multi-year Human Factors research program to establish empirically grounded thresholds — identifying precisely where response delay crosses from imperceptible to trust-eroding, and anchoring those findings in the economic models that drive product investment.
Isolating the Latency Satisfaction Threshold
Used a Wizard of Oz latency injection system to manipulate visual and auditory response latency on users' devices across thousands of trials — controlling latency as an isolated variable while keeping all other system factors constant. Analysis of the resulting data identified a High Satisfaction Threshold (HST) at 1,000ms: the point where user tolerance breaks down, "too slow" ratings climb sharply, and satisfaction drops in a consistent, reliable pattern.
NVA Model Integration & Economic Validation
The HST was formally adopted by Alexa Economics and embedded into the Negative Value Action (NVA) model — redefining any response exceeding 1,000ms as a quantifiable system defect with downstream engagement consequences. Alexa Economics projected that closing the latency gap across high-priority voice intents would generate $50M in incremental operational value over 15 months — a third-party validation that became the foundation of Alexa's Latency North Star.
NVA Model
Source: Alexa Response Latency Research — Devices Design Group, April 2021
Evolving established models into modern Human-AI trust frameworks
Where cognitive error costs lives, not conversions
Translating trust frameworks to human-machine interfaces