Movement Intelligence

The pattern repeats across a decade of machine learning: stop hand-crafting features, train one model on the raw signal at scale, and every downstream task improves at once. Text went through it. Images went through it. Protein structure went through it.

Human movement has not. Yet it is one of the richest signals a body produces — the integrated output of the entire musculoskeletal and nervous system, carrying information no blood test, questionnaire, or wearable can capture. The raw signal exists — skeleton sequences, twenty-one joints, twenty-five frames per second — but the field still runs on hand-picked metrics: distances, speeds, counts. That gap is the company.

Tracking systems answer where: positions, distances, sprints. The skeleton answers how: stride mechanics, deceleration control, left–right asymmetry, coordination under fatigue. That second layer is where form changes, habits show, and risk accumulates — and it is invisible in a dot on a plane.

WithoutBall learns this layer directly from raw skeleton sequences — self-supervised, no hand-labeled events. What it makes possible is catalogued under capabilities; how it is built is documented in the docs.