论文标题
在医疗保健中探索人类活动识别中的对比度学习
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
论文作者
论文摘要
鉴于它在人类的福祉和健康监测中的影响，人类活动识别（HAR）构成了可穿戴和移动感的最重要的任务之一。这项工作是受HAR中标记的数据集的局限性的限制，尤其是在与医疗保健相关的应用中使用时，这项工作探讨了Simclr的采用和适应性（一种对视觉表征的对比学习技术）的采用和适应性。对比度学习目标的使用导致相应观点的表示形式更加相似，而无与伦比的观点的表示则更加不同。在进行了广泛的评估，探索了64种不同信号转换以增强数据的组合后，我们观察到由于顺序及其功能而显着的性能差异。特别是，初步结果表明，在使用微调和随机旋转进行增强时，对监督和无监督的学习方法有所改善，但是，未来的工作应探索SIMCLR对HAR Systems和其他与医疗保健相关的应用程序有益的条件。
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.
