Inertia-1 sweeps wearable-pretraining design choices and evaluates transfer across 15 downstream datasets

Beyond establishing pretraining scale (see claim-inertia-1-pretrained-18-million-hours-accelerometer), the Inertia-1 paper (arXiv:2607.06617) is framed as a systematic exploration of the design space for wearable motion foundation models rather than a single model release. The authors sweep three families of choices: "data choices such as sensor modality, device placement, sampling rate, window length; model choices such as architectures and model size; and training choices such as pretraining objective and data scale."

To measure the payoff of those choices, they evaluate transfer across 15 downstream datasets spanning three task families: human activity recognition, freezing-of-gait detection, and disease prediction. This breadth is the paper's evidence base for claims about what a wearable-sensing pretraining recipe should look like — the wearable analogue of testing an LLM's learned representation against a benchmark suite.

[unverified-quant/mechanism — needs primary read of the paper body.] The capture that produced this note was built from the abstract alone. Which specific design choices actually won — which architecture, which pretraining objective, how much of the transfer gain comes from data scale versus model size, and by what margins — was not extracted, because the abstract does not state it. The methodology described here (the sweep dimensions and the 15-dataset, three-task evaluation) is Tier-1 and quotable; the outcomes of the sweep are not yet in the vault. That verification is routed to question-inertia-1-which-design-choices-win.

2026-07-18 addendum — resolved. A follow-up capture read the paper body (Results and Analyses, §4) directly and the sweep's outcomes are now six claim-notes: 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. The [unverified-quant/mechanism] flag above is resolved for the outcomes it named; it is left in place rather than deleted so the note's history stays legible.