  The development of compact and energy-efficient wearable sensors has led to
an increase in the availability of biosignals. To analyze these continuously
recorded, and often multidimensional, time series at scale, being able to
conduct meaningful unsupervised data segmentation is an auspicious target. A
common way to achieve this is to identify change-points within the time series
as the segmentation basis. However, traditional change-point detection
algorithms often come with drawbacks, limiting their real-world applicability.
Notably, they generally rely on the complete time series to be available and
thus cannot be used for real-time applications. Another common limitation is
that they poorly (or cannot) handle the segmentation of multidimensional time
series. Consequently, the main contribution of this work is to propose a novel
unsupervised segmentation algorithm for multidimensional time series named
Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to
work easily with both online and batch data. When comparing LS-USS against
other state-of-the-art change-point detection algorithms on a variety of
real-world datasets, in both the offline and real-time setting, LS-USS
systematically achieves on par or better performances.
