DimensionsPot
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Anthropometric API 130 Body
Dimensions. One API Call. No Photos. No PII. Stateless.

Supply height and weight, or any other body measurement. Get back a complete anthropometric profile — 130 ISO-standard dimensions, calibrated confidence scores, and 95% prediction intervals. Not AI — peer-reviewed statistical models on real anthropometric data. GDPR and HIPAA-ready.

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130
Dimensions
Full body profile per call · ISO codes where available
5
Body Bundles
Full Body
Torso
Head & Face
Hand & Arm
Legs & Feet
3
Build Types
Civilian
Athletic
Overweight
7
Regional Profiles
Global
Europe
Asia Pacific
Latin America
India
Africa
Middle East
0
PII Stored
Stateless by architecture

No Black Box

Not AI.
Peer-reviewed science.

DimensionsPot doesn't guess. It applies peer-reviewed statistical models to ISO-standardised anthropometric survey data. Every output is traceable — to the dataset, the model, and the equation. No neural network. No training data. No probabilistic hallucination.

Every dimension comes with a confidence score, a 95% prediction interval, and biological limit flags — plus an ISO 7250-1 code where the standard defines one. If you need to know why the model returned a specific value, you can audit it. That's the difference between a statistical model and a black box.

ISO 7250-1:2017

The Standard

Every dimension maps to the international standard for basic human body measurements — the same framework used in ergonomic product certification and occupational safety.

ANSUR II · CDC/WHO · NHANES

Peer-Reviewed Datasets

Models built on rigorously collected, publicly documented anthropometric surveys. No proprietary training data. No undisclosed sources.

Ridge · LMS Box-Cox

Published Methods

Adult predictions use Ridge Regression on ANSUR II. Pediatric uses LMS Box-Cox calibrated to CDC and WHO growth standards. Both are well-understood, published statistical methods.

Score · Interval · ISO Code

Explainable Output

Every dimension returns a confidence score and a 95% prediction interval. ISO 7250-1 codes are included where the standard defines them. The model never returns a number without telling you exactly how certain it is.

How it works

From a few numbers
to a full body profile

01

Send a POST request

Supply gender and at least one measurement. Height and weight together give the best accuracy — but a single anchor is sufficient. Metric and imperial both accepted.

body_height: 1780mm body_mass: 82.0kg
02

Dual-Core Inference runs

The Adult Ridge Regression engine (ANSUR II) or Pediatric LMS Box-Cox model (CDC/WHO) generates predictions through a 9-step pipeline. Missing anchors are reconstructed via dynamic imputation.

9-step pipeline Deterministic · Stateless
03

Receive 130 dimensions

Each dimension includes a value, type (BONE/FLESH), Confidence Score, biological limit status, and optional 95% prediction interval with ISO 7250-1 code.

confidence_score: 85 range_95: [954, 1070]

Capabilities

Built for production,
designed for privacy

Stateless by Architecture

No PII ever stored, logged, or retained between calls. Data is processed in-memory and discarded. GDPR and HIPAA-ready not as a policy, but as a structural fact.

Privacy Architecture

130 ISO 7250-1 Dimensions

Skeletal landmarks, soft-tissue measurements, body composition — defined using ISO 7250-1 anatomical methodology, with standard codes where the ISO specification assigns them. Directly usable for product certification and ergonomic design.

Data Dictionary

Body Bundles

Request only what you need — FULL_BODY (130 dimensions), or named subsets: HEAD_FACE, HAND_ARM, TORSO, LEGS_FEET. Reduces payload size and simplifies downstream processing.

Bundle Reference

Calibrated Confidence Score

Every output dimension carries a Confidence Score [0–100] and an optional 95% prediction interval. The system never over-promises — actual coverage ≥ stated score.

Confidence Score

7 Regional Profiles

Separate calibration for GLOBAL, EUROPE, ASIA_PACIFIC, LATAM, INDIA, AFRICA, and MIDDLE_EAST. Input origin and output target are fully independent fields.

Regional Calibration

Body Build Types

Three morphological presets — CIVILIAN (general population), ATHLETIC (lean, reduced soft-tissue shift), and OVERWEIGHT (BMI-stratified NHANES morphing) — applied before inference to match your customer profile.

Build Types

Anchor Tiers & Confidence

The more you supply,
the more precise
the result

Every dimension carries a Confidence Score [0–100]. BONE skeletal landmarks are more predictable than FLESH soft-tissue measurements. The system is calibrated to never over-promise — stated coverage ≥ actual coverage.

Each output also optionally returns a 95% prediction interval (range_95) — the statistical range within which the true measurement falls for 95% of individuals with the same inputs. Useful for tolerance stacking, sizing logic, and quality control.

Anchor tiers & confidence scoring
Tier Input supplied BONE FLESH
PRIMARY_RICH Height + weight + ≥1 circumference ~87 ~80
PRIMARY_BOTH Height + weight ~85 ~78
PRIMARY_ONE Height OR weight only ~79 ~62
SECONDARY Foot length, knee height… ~74 ~67
TERTIARY Any other single measurement ~69 ~62

Pediatric Module — included

Children aren't
small adults.

A dedicated Pediatric LMS Box-Cox model calibrated against CDC and WHO growth standards. Unlike adult regression, pediatric inference accounts for non-linear growth curves across each developmental stage — with separate confidence scoring at every tier.

Age or age category alone is sufficient — no body measurements required

Separate confidence scoring calibrated to pediatric growth variability

Stateless architecture — no PII stored, COPPA and GDPR-ready by design

Pediatric engine documentation
INFANT
0 – 23 months

WHO Multicentre Growth Reference Study. Non-linear growth at monthly granularity.

TODDLER
2 – 3 yrs

Rapid proportional shifts in trunk and limb ratios accounted for at each age step.

CHILD
4 – 8 yrs

CDC growth charts with LMS transformation. Steady mid-childhood growth curve.

PRE_TEEN
9 – 12 yrs

Pre-pubertal acceleration phase modelled separately for each gender.

TEEN
13 – 20 yrs

WHO adolescent standards with NHANES calibration. Puberty-stage growth modelled.

Use Cases

Built for every vertical
that needs body data

Fashion & Apparel E-commerce

Size recommendations from height and weight alone. No fitting room, no photos. Reduce bracket buying and return rates at scale.

Brief Cookbook

Gaming, VFX & Metaverse

Regionally calibrated skeletal dimensions for Unity Humanoid and Unreal MetaHuman. Human-accurate avatars from minimal input.

Brief Cookbook

Online Eyewear & VR/AR Headsets

IPD, head breadth, face length, bridge width — without a photo. Predict head-fit dimensions for eyewear and VR/AR headsets.

Brief Cookbook

Sport Equipment & Outdoor Gear Rental

Pre-size rental equipment from customer-provided height and weight. Eliminate in-store fitting queues and sizing errors.

Brief Cookbook

Wearables & Smart Accessories

Smartwatch bands, fitness trackers, smart rings auto-sized from height alone. Eliminate sizing returns at checkout.

Brief Cookbook

Childrenswear & Children's Products

CDC/WHO-calibrated profiles for ages 0–20. INFANT through TEEN categories. Zero body measurements required.

Brief Cookbook

Workwear at Scale

Size entire workforces from HR data without measurement sessions. Reduce labor costs and human error in bulk procurement.

Brief Cookbook

Global Retail & Multi-Region Platforms

Regional calibration for 7 population profiles. Eliminate systematic sizing errors when selling across markets.

Brief Cookbook

Simple to Integrate · Private by Architecture

DimensionsPot vs
Photo-Based Sizing

Two numeric inputs. Minutes to integrate. No photos, no biometric data, no GDPR special categories. The simplicity and privacy advantage is structural — not a policy.

DimensionsPot
Photo-based APIs
User input
Height + weight (optional: any circumference). Two numeric fields.
2 full-body photos, strict lighting & pose required
Data stored
Nothing — stateless by architecture. No logs, no retention, no user profiles.
Photos retained for model processing and retraining
Privacy & compliance
No biometric data. No GDPR Article 9 obligations. No EU AI Act biometric classification.
Photos = GDPR Art. 9 biometric data. Strict EU AI Act obligations apply.
Integration effort
One POST request. 2 numeric input fields. Any language — no SDK required.
Photo upload flow, CDN, storage, moderation, pose-validation layer
Time to first call
Minutes — subscribe on RapidAPI, copy key, send first request.
Days to weeks — build photo infra, consent UX, moderation pipeline
Infrastructure
None required. Standard HTTPS POST — no storage, no media pipeline.
Storage layer, CDN, image processing, model hosting required

Photographs are biometric data under GDPR Article 9 — a special category requiring explicit legal basis and strict safeguards. Any link between a body profile and a specific individual exists exclusively in your own infrastructure.

Pricing

Start free.
Scale when you're ready.

All features available on every tier. No feature gating — plans differ only in monthly request volume. A practical alternative to enterprise photo-based sizing platforms.

Free
$0/mo
100 req / month
Proof of concept, testing
Start Free
Starter
$79/mo
2,000 req / month
Small e-commerce, indie developers
Subscribe
Most popular
Pro
$299/mo
10,000 req / month
Growth-stage platforms & SaaS
Subscribe
Business
$799/mo
50,000 req / month
Large retailers, enterprise integrations
Subscribe

Costs you avoid

Photo hosting and CDN infrastructure
Image storage and data retention
Biometric consent UX development
GDPR Article 9 compliance engineering
Photo moderation pipeline
Pose-and-lighting validation layer
Integration and maintenance overhead

Need unlimited calls or on-premise deployment? Contact us for Enterprise →

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Free tier — 100 requests/month, no credit card

Start building
in under 5 minutes

Subscribe on RapidAPI, use the pre-filled Playground example, and receive your first 130-dimension body profile. No SDK required.

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Disclaimer: All outputs of the DimensionsPot API are statistically derived anthropometric predictions intended to support — not replace — professional judgment. They do not constitute medical, clinical, ergonomic, or professional advice. The Confidence Score is a proprietary heuristic index — not a statistical confidence interval. To the fullest extent permitted by applicable law, DimensionsPot disclaims all liability for any damages arising from reliance on API outputs.