  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. We proposed and evaluated a workflow in which
a model learns to extract informative features using a corpus of unlabeled time
series data and then conducts classification on labelled data using features
extracted by the model. We analyzed the effect of varying the size,
distribution, and source of the unlabeled data on the final classification
performance across four public datasets, including various types of sensors in
diverse applications.
