Some systems train correct reactions.
Others train delay.
Simulation is not defined by how much a system moves, how large the visuals are, or how immersive it appears. It is defined by whether the system delivers motion, timing, and sensory alignment in a way the brain recognizes as real.
These three pages introduce the framework in plain language. No prior knowledge of simulation, physics, or neurophysiology required.
What the SFR framework is, what problem it solves, and the structural divide between simulation types. A 4-minute plain-language overview.
Read introduction →The classification gap, the measurement gap, and the language gap that made a formal standard necessary. The full narrative.
Read the story →How the framework applies to your specific context: buyers, researchers, motorsport programs, rehabilitation, medical professionals, and more.
Find your context →Understanding the framework is the first step. These four pages show how the classification process works in practice — with step-by-step examples, plain-language explanations, and an interactive self-assessment.
What an SFR evaluation is, how it works, and what it produces. Gateway to both reference examples.
Explore evaluations →Step-by-step evaluation of a hypothetical physics-derived system. All three criteria pass. Classification: In-the-Loop.
Read evaluation →Step-by-step evaluation of a hypothetical hexapod platform with washout filtering. Criteria A and B fail. Classification: Surface-Level.
Read evaluation →Five plain-language questions about your system's architecture. Outputs a likely classification tier. Educational only.
Start assessment →SFR is grounded in established research disciplines — but it has not yet been empirically validated as a classification standard. This section explains what exists, what has been done, and what remains to be done.
Central overview of the framework's evidence basis, current initiatives, and validation path. Includes transparency statement.
Read overview →The six research disciplines that informed SFR's structural criteria, with an explicit account of the distinction between influence and validation.
Read influences →A five-stage path from proposed standard to formally validated standard. Current status, what each stage requires, and what has been completed.
Read roadmap →How universities, human performance labs, and rehabilitation researchers can engage with future validation activities. Informational only.
Read overview →The issue is not movement alone. The issue is whether the system delivers correct timing, motion origin, and sensory alignment.
What Most Simulators Get Wrong →If the physics are not in the loop, neither is the driver.
| Criterion | In-the-Loop | Surface-Level |
|---|---|---|
| Motion Origin | Driven directly by vehicle physics state | Applied as effect; not derived from state |
| Center-of-Mass Alignment | Motion resolved at the vehicle's true center of mass | Rotation occurs at incorrect point; not CoM-referenced |
| Degrees of Freedom | Independent axes; each resolves separately and correctly | Coupled or blended; axes cannot be independently isolated |
| Yaw Fidelity | Present, continuous, and correctly timed | Absent, delayed, or approximated |
| Vestibular Validity | Physical cues match vehicle event timing | Cues absent, approximated, or delayed |
| Training Outcome | Correct timing and response patterns trained | Delayed, visual-dependent, or incorrect patterns trained |
Simulation must be defined before it can be measured, measured before it can be classified, and classified before its consequences can be understood.
This framework defines how simulation systems can be evaluated based on structure, motion, and training relevance. It is designed for engineers, researchers, and organizations seeking measurable clarity.
SFR is a structured way to evaluate whether a simulation system delivers physically and neurologically valid training.
| System Type | SFR Profile | Training Validity |
|---|---|---|
| True CoM Independent DOF System | High | Valid |
| Stewart Platform / Hexapod | Limited | Partial |
| Seat Mover | Limited | Partial |
| Static Simulator | Low | Invalid |
Classification matters because simulation is not neutral. A system either reinforces correct timing and perception, or it trains deviation from them.
If the system introduces timing gaps between input and sensory response, the driver learns to act on a delayed signal.
Repetition against wrong physical relationships builds responses that do not match the real vehicle.
Training that does not preserve correct structural relationships cannot reliably transfer to real vehicle operation.
Each session in an incorrectly structured system reinforces the misalignment rather than correcting it.
If you train late, you react late.
View ConsequencesSimulation quality affects far more than driver comfort or immersion. It affects training validity, decision timing, engineering interpretation, and neurological outcome.
The SFR framework is available for reference, citation, procurement application, and research use today as a proposed standard. A full set of adoption resources covers how different organizations engage with the framework and what each context requires.
Simulation architecture determines more than training efficiency. It determines what the nervous system receives, how it adapts, and whether learned responses transfer to real performance. The Human Outcomes Framework traces these downstream effects from sensory input through to real-world outcome.
Layer overview and chain
What the nervous system receives
How inputs are integrated
How the system changes over time
Whether outcomes transfer to reality
Simulation is not defined by spectacle.
It is defined by whether it matches reality.
If the timing is wrong, the training is wrong.
Apply the framework to a real system, environment, or use case through a structured review pathway.
For teams, facilities, researchers, and organizations seeking structured classification or review.