  Internet of things (IoT) applications have become increasingly popular in
recent years, with applications ranging from building energy monitoring to
personal health tracking and activity recognition. In order to leverage these
data, automatic knowledge extraction - whereby we map from observations to
interpretable states and transitions - must be done at scale. As such, we have
seen many recent IoT data sets include annotations with a human expert
specifying states, recorded as a set of boundaries and associated labels in a
data sequence. These data can be used to build automatic labeling algorithms
that produce labels as an expert would. Here, we refer to human-specified
boundaries as breakpoints. Traditional changepoint detection methods only look
for statistically-detectable boundaries that are defined as abrupt variations
in the generative parameters of a data sequence. However, we observe that
breakpoints occur on more subtle boundaries that are non-trivial to detect with
these statistical methods. In this work, we propose a new unsupervised
approach, based on deep learning, that outperforms existing techniques and
learns the more subtle, breakpoint boundaries with a high accuracy. Through
extensive experiments on various real-world data sets - including
human-activity sensing data, speech signals, and electroencephalogram (EEG)
activity traces - we demonstrate the effectiveness of our algorithm for
practical applications. Furthermore, we show that our approach achieves
significantly better performance than previous methods.
