A public-facing map of the systems behind CoLab: motion proof, athlete analytics, Vision Analysis, coaching loops, academy packaging, and commerce paths that keep the next action attached to what the athlete just did.
This page explains what CoLab has built without publishing private benchmark tables, admin-only controls, provider details, or fragile heuristics. LAB is the primary analytics contract. POWER☲, Trace-era artifacts, and other legacy fields are supporting context.
Movement proof
A coaching layer that turns short athlete video into movement phases, scored context, cue paths, and next actions. Trace remains legacy/internal naming; the public product language is ECHO.
Where it shows up
Clip capture, phase review, coach cue routing, team packages.
Canonical analytics
The athlete decision anchor: Load, Adapt, and Bounce. LAB keeps readiness, learning quality, and motion quality in one explainable hierarchy with confidence attached.
Where it shows up
Athlete status, coach snapshots, analytics summaries.
Supporting views
LAB feature views and POWER☲ profiles are useful drill-downs for skill learning, sport-specific work, goals, and capacity. They should support LAB rather than compete with it as the headline score.
Where it shows up
Performance sheets, power profile cards, coach context.
Cue response
A cue and response path built from saved ECHO sources. HERO carries the coaching moment into voice, narrative, feedback, and the next athlete action.
Where it shows up
Academy feedback, clip response, coach narration.
Strategy engine
Sport-specific pattern cards and heatmaps for tennis, swimming, and triathlon. Patterns turn athlete context into practical structures, pressure scenarios, cues, and risks.
Where it shows up
Playbook strategy, academy pattern generation, heatmaps.
Video to plan
A closed loop that moves from Filmroom clip to ECHO, Trace, Mirror, and HERO signals, then into Loop Chat explanation, Vision Library memory, protocol, and product recommendations.
Where it shows up
Filmroom vision, Loop Chat, library memory, daily protocols.
Reference intelligence
A GOAT/reference movement model for comparing athlete motion to sport-specific ideals, without reducing coaching to imitation. It identifies meaningful gaps and phase priorities.
Where it shows up
Reference scoring, GOAT comparison, movement roadmap.
Comparison layer
Mirror aligns two clips for side-by-side review around the same motion question.
Where it shows up
Filmroom sync, phase review, coach review.
Clip substrate
A short-form capture and taxonomy layer for sport, view, skill, environment, athlete, and tags. BURST keeps raw movement moments small enough to analyze and compare.
Where it shows up
Filmroom upload, clip intake, ECHO source prep.
Video tests
POWER☲ video-derived jump, agility, broad jump, and VBT testing that feeds motion-quality signals into Bounce and broader LAB analytics.
Where it shows up
Testing page, training toolkit, movement summaries.
Knowledge loop
A creator-knowledge and athlete-context loop that turns uploaded methods, session state, products, and performance material into practical coaching next actions.
Where it shows up
Smart coach, Loop agent, academy-specific assistants.
Review workflow
The video review layer that keeps clip capture, mirror comparison, ECHO output, coach notes, roster context, and the next session in one workflow.
Where it shows up
Filmroom, Performance Lab, team review.
Runtime bands
Versioned sport, skill, phase, and view-specific benchmark profiles that make vision analysis readable as ranked coaching context.
Where it shows up
Vision Analysis cards, ECHO phase rankings.
Ball and rotation
A ball-sport and rotational scorecard for line, separation, sequencing, velocity, lower-body power, upper-body power, and sport expression.
Where it shows up
Tennis, golf, ball motion, rotational skill review.
Human signal R&D
Experimental affect, movement-context, and mirror-neuron research surfaces. These labs explore how attention, joy, motor context, and perception can support learning.
Where it shows up
R&D, future learning signals, academy experiments.
Spatial proof
Spatial rendering and reconstruction workflows that turn movement media into depth-aware, reviewable scenes and package-ready learning assets.
Where it shows up
ECHO spatial, XR pipeline, 6DoF package outputs.
Commerce to performance
Reusable product storytelling systems that connect gear, memberships, training blocks, recovery, content, and checkout to the athlete's performance context.
Where it shows up
Product pages, kits, concept offers, branded stacks.
Entitlement IP
Membership, assignment, sponsored-team, and academy access flows that package coaching knowledge, ECHO content, and team workflows around who should see what.
Where it shows up
Academies, team licenses, ECHO assignments.
Vision Analysis is the public product loop that connects Filmroom clips, ECHO movement proof, Mirror comparison, HERO cue response, Loop Chat explanation, Vision Library memory, protocols, and product recommendations.
Closed loop
Capture, analyze, explain, remember, and act without exposing private benchmark tables or scoring weights.
Capture
The athlete or coach uploads, imports, trims, or records the movement so the analysis starts from a real rep.
Analyze
The clip becomes keyframes, phase scores, reference deltas, bottleneck cues, and spatial explainer output.
Explain
Structured analysis becomes constrained cards for evidence, gap, protocol, caveat, and next action.
Remember
Athlete clips, coach references, ECHO packages, GOAT-style systems, and retests stay attached to the training history.
Act
The loop ends in a daily plan and only recommends products when the gear has a training job today.
Consume structured analysis JSON and return constrained coaching cards instead of loose advice.
The public page can show one loop while implementation keeps analysis runs, comparisons, and plans as separate owned flows.
Exact athlete or elite comparisons require owned or licensed reference assets, phase labels, and comparable camera context.
Single-camera clips can support guided overlays; true 6DoF claims need depth, multi-view, 3D, or mocap-quality data.
Use confidence, calibration, FPS, and validation data before presenting radar-grade or universal performance claims.
Benchmarks
The Loop coach should explain why a product belongs in today's work, what it replaces, who should avoid it, and how it fits the kit. That keeps commerce connected to training context instead of becoming a generic product grid.
Protect novel technical video-to-delta-to-protocol processes only where the method is concrete and more than an abstract idea.
35 U.S.C. 101Keep scoring weights, curated reference labels, calibration methods, prompts, ranking logic, and eval recipes private when secrecy creates value.
18 U.S.C. 1839Human-authored curriculum, edited videos, UI copy, and curated arrangements are the cleanest protectable expression.
U.S. Copyright OfficeNamed athletes are safer as factual or archetype references unless reference media, likeness, and endorsement rights are secured.
California Civil Code 3344Financial relationships and product recommendations need obvious disclosures, substantiated claims, and clear training relevance.
FTC guidanceOperator surface
Internal workspaces for validating Gnosis, vision bands, Muse Joy, Neuro, XR, ECHO metadata, and product systems.
Movement surface
The clip review and motion stack layer: BURST intake, Mirror, ECHO packages, spatial review, and coach-facing proof.
Athlete surface
The applied performance layer for POWER☲, Bounce, LAB, patterns, readiness context, personal bests, and session planning.
Public product surface
The public framing for Smart Loop, Loopmark, ECHO Studio, GOAT references, blocks, stacks, and the product narrative.
Packaging surface
The route layer for kits, memberships, academies, team access, branded products, and commerce attached to performance use.
Public movement product
Use ECHO for Motion Stack Analysis. Treat Trace as legacy/internal terminology where older code and package paths still exist.
Headline analytics contract
LAB should remain the primary athlete status model. Readiness, POWER☲, Trace, and legacy scores belong underneath it.
Supporting capacity + testing view
POWER☲ explains strength, work, capacity, mobility/stability context, and the video-test layer for jump, agility, broad jump, and VBT captures that feed Bounce and LAB. It should not replace LAB as the main decision anchor.
Comparison language
Mirror is the public-friendly language for syncing two videos around the same motion question.
Biomechanical pillar
Bounce is the user-facing biomechanical state inside LAB and replaces older internal CME language.
Context loop
Loop is the cross-product pattern that keeps plan, proof, product, coach, and next action connected.
Keep this page focused on product methods, language, and system relationships. Legal filings, exact score weights, private benchmark tables, and admin-only validation controls should remain in internal documents.
