What is a Large Movement Model?
The Large Movement Model (LMM) is a general-purpose AI system built around human motion as its core data type. Instead of learning from words, LMM learns from sequences of movement—patterns of joints, limbs, balance and timing— allowing it to recognize activities, forecast what comes next, and evaluate the quality of motion across many real-world settings.
Think of it as “an LLM for motion”: a single model that can be adapted to digital health, safety, sports, robotics, and other domains where how people move actually matters.
Why Motion, Why Now?
Text, images, and audio all have mature foundation models. Human motion does not. Today, most systems that reason about movement are narrow, one-off solutions: one model for rehab, another for sports, another for security. LMM is designed to change that by treating motion itself as a first-class sequence domain.
Anchor Application Domains
We are actively developing and validating LMM across three anchor verticals, with additional domains to follow:
- Digital Health & Rehabilitation – Gait and balance analytics, compensation and fall-risk signatures, and objective progress tracking from routine rehab sessions.
- Security & Safety Intelligence – Motion-aware anomaly detection and behavior understanding for CCTV, with a focus on privacy-preserving, pose-based analysis instead of identity.
- Sports Analytics – Biomechanics, fatigue and injury-risk indicators, and team-level motion patterns that drive spacing, effort, and game dynamics.
The same underlying model family can also be adapted to collaborative robotics, AR/VR avatars, human–AI interaction, and other embodied AI use cases as the ecosystem matures.
How LMM Works (High Level)
LMM operates on normalized representations of human motion derived from video and other sensors. These sequences are turned into compact motion “tokens” that a transformer-style model can process, much like language tokens in an LLM. The model learns:
- How activities unfold over time (from micro-movements to longer behaviors)
- What constitutes physically plausible, biomechanically consistent motion
- How to predict near-term futures and complete partial or occluded motion
Implementation details—specific datasets, tokenization schemes, architectures, and training strategies—are part of our internal R&D program and partner discussions.
Partnerships & Pilots
LMM is being developed by Aegis Station Infrastructure LLC as an early entrant in motion-centric foundation models. We’re currently:
- Building and testing LMM prototypes on curated motion datasets
- Exploring research collaborations with universities and labs
- Scoping pilot projects with organizations in rehab, security, and sports
If you are interested in partnering on research, pilots, or future licensing, we’d be glad to talk.
Contact
For inquiries about partnerships, pilots, or staying informed as we share results:
engage@aegisstation.com