Inertia-1
An Open Exploration to a Unified Motion Foundation Model
Towards one general motion model
Motion is universal — but the models built for it weren't. Inertia-1 brings the whole landscape under one roof.
Datasets disagree on the basics — sampling rate, window length, sensor modality, body placement, even signal format — and every task gets its own bespoke model. Findings rarely carry from one setup to the next.
Inertia-1 studies the full lifecycle of motion models — data, sensing, objectives, and scale — inside a single, controlled space instead of isolated one-offs.
The payoff: one representation that adapts across placements, devices, and tasks — the same backbone, working far beyond the setting it was trained on.
Beyond benchmarks, Inertia-1 surfaces the choices that decide whether a motion model actually works in the real world.
Learn it on the wrist. Use it anywhere on the body.
Pretrain once on the wrist, then point the model anywhere. It holds up on body placements — and even sensor types like gyroscope and magnetometer — that it never saw during training. No retraining for each new spot on the body.
Add more streams. Get more signal.
Stack on more streams — extra placements, gyroscope, magnetometer — and the learned representation gets both more accurate and cleaner, with activities separating into tighter clusters. The streams are complementary: each one catches something the others miss.
Sensing design is a first-order choice
How you capture motion shapes what a model can do with it. A few practical rules of thumb from the study.
Sampling rate
Pretrained models stay strong even at a low 1 Hz for activity recognition; finer-grained health signals benefit from higher sampling rates.
Pretrained models stay strong even at a low 1 Hz for activity recognition; finer-grained health signals benefit from higher sampling rates.
Window length
30–60 second windows hit the sweet spot across most tasks — long enough to capture context, short enough to stay sharp.
30–60 second windows hit the sweet spot across most tasks — long enough to capture context, short enough to stay sharp.
Keep all three axes
Full triaxial input consistently beats collapsed vector-magnitude summaries — the extra axes carry signal worth keeping.
Full triaxial input consistently beats collapsed vector-magnitude summaries — the extra axes carry signal worth keeping.
Stay in the time domain
Time-domain modeling preserves gait and health cues better than frequency-domain reconstruction.
Time-domain modeling preserves gait and health cues better than frequency-domain reconstruction.
One pipeline, from raw signal to real-world insight
The general representation comes together in three clean steps.
Learn from planetary-scale accelerometry — over 18 million hours across global cohorts — with self-supervision, no labels required.
Adapt the same representation to new placements, devices, and sampling rates with light tuning — or none at all.
Power activity, mobility, and health applications from one backbone — from fitness tracking to clinical screening.
From movement to meaning
The same representation spans the full spectrum of motion understanding.
One general model for human motion
Inertia-1 is a first step toward a unified motion foundation model — and an open invitation to collaborators with motion data, new tasks, or a shared interest in where the field is headed.