RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
Maxwell A Xu Jaya Narain Gregory Darnell Haraldur T Hallgrimsson Hyewon Jeong Darren Forde Richard Andres Fineman Karthik Jayaraman Raghuram James Matthew Rehg Shirley You Ren
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 strong 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.
imu, har, biosignals, activity classification, gait metrics, time-series, foundation model, contrastive learning, self-supervised learning
We present RelCon, a novel self-supervised Relative Contrastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors.