Machine learning algorithms are improving rapidly, but annotating training data remains a bottleneck for many applications. In this paper, we show how real data can be used for self-supervised learning without any transformations by taking advantage of the symmetry present in the activities. Our approach involves contrastive matching of two different sensors (left and right wrist or leg-worn IMUs) to make representations of co-occurring sensor data more similar and those of non-co-occurring sensor data more different. We test our approach on the Opportunity and MM-Fit datasets. In MM-Fit we show significant improvement over the baseline supervised and self-supervised method SimCLR, while for Opportunity there is significant improvement over the supervised baseline and slight improvement when compared to SimCLR. Moreover, our method improves supervised baselines even when using only a small amount of the data for training. Future work should explore under which conditions our method is beneficial for human activity recognition systems and other related applications.

翻译：机器学习算法正快速进步，但训练数据标注仍是许多应用的瓶颈。本文展示了如何利用活动本身固有的对称性，在无需任何数据变换的情况下，将真实数据用于自监督学习。我们的方法通过对比匹配不同传感器（左右手腕或腿部佩戴的惯性测量单元），使同时发生的传感数据表征更相似，而非同时发生的传感数据表征更具差异性。我们在Opportunity和MM-Fit数据集上测试了该方法。在MM-Fit数据集中，本方法较有监督基线方法和自监督方法SimCLR表现出显著提升；而在Opportunity数据集中，相较于有监督基线方法有显著改进，与SimCLR相比亦有小幅提升。此外，即使用少量数据进行训练，本方法也能提升有监督基线性能。未来工作应探索该方法在何种条件下能有效赋能人体活动识别系统及其他相关应用。