---
title: "Inertia-1"
type: "entity"
entity_kind: "concept"
status: "hub"
canonical_name: "Inertia-1"
aliases: []
first_seen: "2026-07-09T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["Yuzhe Yang","wearable motion foundation models","self-supervised pretraining","accelerometer sensing","freezing-of-gait detection"]
---


A wearable motion foundation model — arXiv:2607.06617, "Inertia-1: An Open Exploration of Wearable Motion Foundation Models" (Xu, Anand, Jiang, Zhuang, Shuai, Sankararaman, Yang; submitted 7 July 2026) — pretrained on over 18.2M hours of accelerometer data and evaluated across 15 downstream datasets spanning human activity recognition, freezing-of-gait detection, and disease prediction. It anchors the vault's wearable/health-foundation-model cluster: the paper's own systematic design sweep (data choices, model choices, training choices) is now the vault's most-documented single source on what actually moves the needle for wearable-sensor pretraining.

## References
- [[claim-inertia-1-pretrained-18-million-hours-accelerometer]] · [[claim-inertia-1-design-sweep-15-downstream-datasets]] · [[claim-inertia-1-data-scale-beats-model-size]] · [[claim-inertia-1-triaxial-beats-vector-magnitude-input]] · [[claim-inertia-1-no-pretraining-objective-dominates]] · [[claim-inertia-1-self-supervised-beats-supervised-gap-widens]] · [[claim-inertia-1-downsampling-robustness-is-task-dependent]] · [[claim-inertia-1-no-single-window-length-is-optimal]]
- Related hubs: [[entity-yuzhe-yang]] · [[entity-freezing-of-gait-detection]]