Exploring Contrastive Learning in Human Activity Recognition for Healthcare
This work addresses the problem of limited labeled data for Human Activity Recognition in healthcare applications, offering an incremental improvement for HAR system developers.
This paper explores the adaptation of SimCLR, a contrastive learning technique, for Human Activity Recognition (HAR) in healthcare, motivated by limited labeled datasets. After evaluating 64 combinations of signal transformations for data augmentation, preliminary results showed an improvement over supervised and unsupervised learning methods when using fine-tuning and random rotation.
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.