Controlled large-scale pretraining on 18.2M hours of wearables shows self-supervised motion models beat scratch training, with triaxial fidelity, data diversity, and task-matched windows mattering more than model size alone.
Wear: An outdoor sports dataset for wearable and egocentric activity recognition, 2024
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Inertia-1: An Open Exploration of Wearable Motion Foundation Models
Controlled large-scale pretraining on 18.2M hours of wearables shows self-supervised motion models beat scratch training, with triaxial fidelity, data diversity, and task-matched windows mattering more than model size alone.