Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition

Understand

Human Activity Recognition (HAR) systems have been extensively studied by the vision and ubiquitous computing communities due to their practical applications in daily life, such as smart homes, surveillance, and health monitoring.

• Typically, this process is supervised in nature and the development of such systems requires access to large quantities of annotated data.
• However, the higher costs and challenges associated with obtaining good quality annotations have rendered the application of self-supervised methods an attractive option and contrastive learning comprises one such method.
• However, a major component of successful contrastive learning is the selection of good positive and negative samples.