Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring. Motivated by the limitations of labeled datasets in HAR, particularly when employed in healthcare-related applications, this work explores the adoption and adaptation of SimCLR, a contrastive learning technique for visual representations, to HAR. The use of contrastive learning objectives causes the representations of corresponding views to be more similar, and those of non-corresponding views to be more different. After an extensive evaluation exploring 64 combinations of different signal transformations for augmenting the data, we observed significant performance differences owing to the order and the function thereof. In particular, preliminary results indicated an improvement over supervised and unsupervised learning methods when using fine-tuning and random rotation for augmentation, however, future work should explore under which conditions SimCLR is beneficial for HAR systems and other healthcare-related applications.

翻译：人类活动认识(HAR)是穿戴和移动感应的最重要任务之一,因为它对人类福祉和健康监测的影响。由于HAR的标签数据集有限,特别是在与保健有关的应用中使用时,这项工作探索了SimCLR的采用和改造,SimCLR是一种用于视觉表现的对比式学习技术,采用HAR。使用对比式学习目标使得相应观点的表述更为相似,非对应观点的表述更为不同。在广泛评估探索了64种不同信号转换组合以扩大数据之后,我们观察到由于秩序及其功能而存在显著的性能差异。特别是,初步结果显示,在使用微调和随机轮换来增强能力时,对监督和不受监督的学习方法有了改进。但是,今后的工作应该探索,在哪些条件下,SimCLR对HA系统和其他与保健有关的应用有利。