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RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
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We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors. First, a learnable distance measure is trained to capture motif similarity and domain-specific semantic information such as rotation invariance. Then, the learned distance provides a measurement of semantic similarity between a pair of accelerometry time-series, which we use to train our foundation model to model relative relationships across time and across subjects. The foundation model is trained on 1 billion segments from 87,376 participants, and achieves state-of-the-art performance across multiple downstream tasks, including human activity recognition and gait metric regression. To our knowledge, we are the first to show the generalizability of a foundation model with motion data from wearables across distinct evaluation tasks.
Forward citations
Cited by 4 Pith papers
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Inertia-1: An Open Exploration of Wearable Motion Foundation Models
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A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention
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Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
A foundation model of wearable behavioral data outperforms simple baselines and complements a PPG sensor model across 57 health detection tasks.
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LSM-2: Learning from Incomplete Wearable Sensor Data
LSM-2 with Adaptive and Inherited Masking learns usable representations directly from incomplete day-long wearable data, outperforming imputation-based baselines on most tasks.
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