---
title: "Inertia-1 and wearable motion foundation models"
type: "moc"
tags: ["foundation-models","wearables","health-ai","self-supervised-learning"]
updated: "2026-07-18T00:00:00.000Z"
---


Everything the vault has read from the Inertia-1 paper (arXiv:2607.06617) — the paper that treats continuous wrist/body accelerometer streams the way LLM pretraining treats text, and the vault's current anchor for wearable-sensing foundation models.

## The model
- [[claim-inertia-1-pretrained-18-million-hours-accelerometer]] — the pretraining scale (18.2M hours)
- [[claim-inertia-1-design-sweep-15-downstream-datasets]] — the sweep's scope: three families of design choices, evaluated across 15 datasets and three task families

## What the sweep found (2026-07-18 promotion)
- [[claim-inertia-1-data-scale-beats-model-size]] — data scale beats model capacity as a transfer lever
- [[claim-inertia-1-triaxial-beats-vector-magnitude-input]] — raw triaxial input beats vector-magnitude compression
- [[claim-inertia-1-no-pretraining-objective-dominates]] — objective choice barely matters for easy tasks, more for hard ones
- [[claim-inertia-1-self-supervised-beats-supervised-gap-widens]] — SSL vs. supervised is a bigger lever than which SSL objective
- [[claim-inertia-1-downsampling-robustness-is-task-dependent]] — pretraining buys sampling-rate robustness for activity recognition, not disease prediction
- [[claim-inertia-1-no-single-window-length-is-optimal]] — window length has no universal optimum either

## Adjacent wearable-sensing work
- [[claim-sc2-tournament-wrist-tremor-declined-over-the-day]] — same sensing modality (wrist accelerometry), a much smaller-scale study
- [[claim-bed-rest-under-runs-spaceflight-bone-loss-depends-on-readout]] — adjacent wearable/physiological-monitoring thread: a different signal, but the same "the design choice determines the readout" shape

## Entities
- [[entity-inertia-1]] · [[entity-yuzhe-yang]] · [[entity-freezing-of-gait-detection]]