Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the largest of its kind to date--containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.

翻译：深度学习在人类活动识别领域的进展相对有限，主要受限于缺乏大规模标注数据集。本研究利用英国生物银行活动追踪数据集——迄今规模最大的同类数据集——该数据集包含超过70万人天的未标注可穿戴传感器数据。我们基于自监督学习技术开发的活动识别模型在七个基准数据集上始终优于强基线模型，F1值相对提升幅度达2.5%-100%（中位数18.4%），其中较小的数据集改进最为显著。与先前研究不同，我们的结果在外部数据集、设备及环境间均具有泛化能力。本开源模型将帮助研究人员和开发者构建高性能、可定制且可泛化的活动分类器。