Regional Calibration

Two independent fields

Regional calibration uses two separate parameters:

• input_origin_region — normalizes input measurements to the ANSUR II global baseline before prediction. Use this when your subject comes from a non-global population and you’re providing measurements you’ve taken.
• target_region — shifts the output dimensions toward the proportions of the target population after prediction. Use this when the end product or recommendation is intended for a specific regional market.

Both fields are independent. A cross-regional request is valid:

"subject": {
  "gender": "female",
  "input_origin_region": "ASIA_PACIFIC"
},
"output_settings": {
  "calculation": {
    "target_region": "EUROPE"
  }
}

This normalizes input anchors from an East Asian baseline, runs inference, then calibrates output for a European market.

Available regions

The Double Penalty Paradox

Without input_origin_region, a non-global subject’s measurements are treated as ANSUR II baseline values. If you then apply a regional output modifier, you double-count the population difference — once from the raw input, once from the modifier.

Setting input_origin_region to the subject’s actual region eliminates this artefact by normalizing input to the global baseline before inference runs.

Body Build Type

body_build_type applies a civilian/athletic/BMI-adjusted body composition shift on top of the regional calibration:

"calculation": {
  "target_region": "EUROPE",
  "body_build_type": "CIVILIAN"
}