Self-supervised pretraining beats supervised training from scratch in Inertia-1, and the margin widens from activity recognition to gait to disease prediction

Answers part of question-inertia-1-which-design-choices-win. Benchmarked against supervised training from scratch on the same data, self-supervised pretraining wins outright in the Inertia-1 paper (arXiv:2607.06617): "self-supervised pretraining consistently outperforms supervised training from scratch across task categories." The paper quantifies the margin as "SSL gain" — the AUROC gap between the best SSL objective and the best supervised baseline — and that gap grows with task difficulty: 4.0 for human activity recognition (HAR), 16.4 for freezing-of-gait (FoG) detection, 19.7 for disease prediction (Table 6).

Read alongside claim-inertia-1-no-pretraining-objective-dominates — which objective you pick barely matters for HAR but the SSL-vs-supervised choice itself is worth up to roughly 20 AUROC points for disease prediction — the two findings together say pretraining paradigm (self-supervised vs. supervised) is a bigger lever than pretraining objective (which SSL method). In the paper's framing: "basic HAR is closer to saturation, whereas clinical and disease-oriented tasks still benefit substantially from representations that capture subtle motion and physiological signatures."