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.
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2 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.
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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.
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Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence
AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.