Description of field of research:

In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available.

Research Area

Deep learning | Time-series | Self-supervised learning

The experiments and the framework will be deployed using Python (Pytorch | Tensorflow)

  1. Continual learning framework for wearable sensors (expansion on our existing works)
  2. 1 or 2 papers on incremental learning with self-supervised pretraining , and self-supervised continual learning (SSCL) on wearable sensor data