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

• URL: http://arxiv.org/abs/2411.18822v4
• Date: Thu, 23 Jan 2025 07:41:13 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 *Rel*ative *Con*trastive learning approach. The learnable distance measure captures motif similarity and domain-specific semantic information. The self-supervised model is trained on 1 billion segments from 87,376 participants from a large wearables dataset.
• Score: 14.097517115921184
• License:
• Abstract: We present RelCon, a novel self-supervised *Rel*ative *Con*trastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors. The learnable distance measure captures motif similarity and domain-specific semantic information such as rotation invariance. The learned distance provides a measurement of semantic similarity between a pair of accelerometer time-series segments, which is used to measure the distance between an anchor and various other sampled candidate segments. The self-supervised model is trained on 1 billion segments from 87,376 participants from a large wearables dataset. The model achieves strong performance across multiple downstream tasks, encompassing both classification and regression. To our knowledge, we are the first to show the generalizability of a self-supervised learning model with motion data from wearables across distinct evaluation tasks.

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