用相对对比学习训练可跨任务通用的可穿戴运动基础模型
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
- 设计可学习距离度量，捕捉运动模式相似性与旋转不变等语义特征
- 在8.7万参与者10亿段数据上训练，下游任务性能达当前最优
- 首次证明可穿戴运动数据基础模型在多任务间的泛化能力
我们提出RelCon，一种用于可穿戴加速度传感器数据的新型自监督相对对比学习方法，用于训练运动基础模型。首先，通过可学习的距离度量捕捉运动片段的模式相似性及领域特异性语义信息（如旋转不变性）；随后，利用该度量提供时间序列间的语义相似性，用于训练基础模型以建模时间与个体之间的相对关系。模型在来自87,376名参与者的10亿个数据段上进行训练，在人体活动识别与步态指标回归等多个下游任务中达到当前最优性能。据我们所知，这是首个展示可穿戴运动数据基础模型在不同评估任务间具备泛化能力的工作。
原文摘要 · Abstract (English)
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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