Human Activity Recognition

Self-Suerpvised Learning (SSL) for sensor-based human activity recognition

While working as a post-doctoral researcher at Maastricht University, I was fortunate enough to be the daily supervisor of Bulat Khaertdinov, a Ph.D. candidate at UM. I guided and collaborated with Bulat to work in Self-Supervised Learning (SSL) for sensor-based Human Activity Recognition (HAR). During my Ph.D. research, when working on Deep Metric Learning, I recognized that SSL could improve the learning process, especially for those tasks (e.g., sensor-based HAR) where data collection and annotation are time-consuming and expensive. We conducted two studies, introducing a novel method based on contrastive-based SSL. Our study also addressed the problem of negative pairs in contrastive learning by using dynamic temperature scaling within a contrastive loss function. The extensive evaluations of three widely used open-source datasets have shown that the proposed method achieves state-of-the-art SSL activity recognition task results. Furthermore, it has demonstrated strong potential in semi-supervised and transfer learning by outperforming many counterpart baseline methods.

This collaboration resulted in the following publications: