  Our ability to exploit low-cost wearable sensing modalities for critical
human behaviour and activity monitoring applications in health and wellness is
reliant on supervised learning regimes; here, deep learning paradigms have
proven extremely successful in learning activity representations from annotated
data. However, the costly work of gathering and annotating sensory activity
datasets is labor-intensive, time consuming and not scalable to large volumes
of data. While existing unsupervised remedies of deep clustering leverage
network architectures and optimization objectives that are tailored for static
image datasets, deep architectures to uncover cluster structures from raw
sequence data captured by on-body sensors remains largely unexplored. In this
paper, we develop an unsupervised end-to-end learning strategy for the
fundamental problem of human activity recognition (HAR) from wearables. Through
extensive experiments, including comparisons with existing methods, we show the
effectiveness of our approach to jointly learn unsupervised representations for
sensory data and generate cluster assignments with strong semantic
correspondence to distinct human activities.
