RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data

• URL: http://arxiv.org/abs/2411.18822v5
• Date: Thu, 10 Apr 2025 22:16:56 GMT
• Title: RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
• Authors: Maxwell A. Xu, Jaya Narain, Gregory Darnell, Haraldur Hallgrimsson, Hyewon Jeong, Darren Forde, Richard Fineman, Karthik J. Raghuram, James M. Rehg, Shirley Ren,
• Abstract summary: We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors.<n>First, a learnable distance measure is trained to capture motif similarity and domain-specific semantic information.<n>We are the first to show the generalizability of a foundation model with motion data from wearables across distinct evaluation tasks.
• Score: 14.097517115921184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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