What is a Large Movement Model?
The Large Movement Model (LMM) is a general-purpose AI system that treats motion itself as the core data type. Instead of learning from words and sentences, LMM learns from sequences of movement—joint positions, trajectories, interactions, and timing across people, tools, and vehicles.
In the same way large language models model how text unfolds, LMM models how motion unfolds: recognizing activities, forecasting what comes next, and evaluating how plausible or “healthy” a movement is across many real-world settings.
You can think of it as an “LLM for motion”—a shared model family that can be adapted to digital health, safety, sports, robotics, and other embodied AI domains.
Why Motion, Why Now?
Text, images, and audio already have mature foundation models. Motion does not. Today, most systems that reason about movement are narrow point solutions: one model for rehab, another for sports, another for driving or security.
LMM is designed to treat motion as a first-class sequence domain, so that the same underlying representation and model family can support:
- Full-body human movement (gait, posture, activities)
- Fine movements of hands, fingers, and face
- Objects and tools (balls, bats, canes, industrial tools)
- Vehicles and macro-objects (cars, forklifts, delivery robots)
- Multi-agent and crowd dynamics (people and vehicles together)
Anchor Application Domains
We are actively developing and validating LMM across three initial verticals, with additional domains to follow:
- Digital Health & Rehabilitation – Gait and balance analytics, detection of compensation and fall-risk signatures, and objective progress tracking from routine rehab sessions and home exercises.
- Security, Safety & Industrial Monitoring – Motion-aware anomaly detection and behavior understanding for CCTV and workplace video, with a focus on privacy-preserving, pose-based analysis instead of identity-first tracking.
- Sports & Performance Analytics – Biomechanics, fatigue and injury-risk indicators, and team-level motion patterns that shape spacing, effort, and game dynamics.
The same underlying motion representation can also be extended to collaborative robotics, AR/VR avatars, human–AI interaction, and other embodied AI use cases as the ecosystem matures.
How LMM Works (High Level)
At a high level, LMM operates on normalized motion sequences derived from video and other sensors. A typical pipeline looks like:
- 1. Capture – Video or multi-camera streams from clinics, courts, gyms, or workplaces.
- 2. Pose & Trajectory Extraction – Pose estimators and trackers produce keypoints for people, hands, faces, tools, and vehicles.
- 3. Normalization – Sequences are centered, scaled, re-timed, and tagged with metadata (domain, confidence, occlusion, temporal scale).
- 4. Motion Tokens – Frames, short windows, and/or learned motion primitives are turned into compact “tokens” analogous to words in language modeling.
- 5. LMM Inference – A transformer-style (and, in future phases, mixture-of-experts and diffusion) model reasons over these tokens to classify, forecast, and score motion.
This architecture allows a single model family to support tasks such as activity recognition, short-term motion prediction, sequence completion, and motion-quality scoring.
How MovementModeler Fits In
MovementModeler is our on-device front door into the LMM ecosystem. It lets users:
- Convert iPhone video into stabilized stick-figure motion clips
- Export BODY-25 JSON for use in research and computer-vision pipelines
- Experiment with smoothing, tracking, and motion visualization without any server setup
Under the hood, the same kinds of normalized skeleton data and export formats that MovementModeler produces are exactly what LMM consumes at larger scale. The app is a practical tool today and a data/interaction bridge into tomorrow’s motion foundation models.
Partnerships & Pilots
LMM is being developed by LMM Technologies Inc., a spin-out of Aegis Station Infrastructure LLC, as an early entrant in motion-centric foundation models. We are currently:
- Building and testing LMM prototypes on curated, de-identified motion datasets
- Collaborating with academic partners on model design, tokenization, and evaluation
- Scoping pilot projects with organizations in rehab, safety/security, and sports analytics
If you are interested in research collaborations, pilot deployments, or future licensing and APIs, we’d be glad to talk.
Contact
For inquiries about partnerships, pilots, or staying informed as we share results:
engage@aegisstation.com