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Efficient Deep Clustering of Human Activities and How to Improve Evaluation
Proceedings of The 14th Asian Conference on Machine
Learning, PMLR 189:722-737, 2023.
Abstract
There has been much recent research on human
activity recognition (HAR), due to the proliferation
of wearable sensors in watches and phones, and the
advances of deep learning methods, which avoid the
need to manually extract features from raw sensor
signals. A significant disadvantage of deep learning
applied to HAR is the need for manually labelled
training data, which is especially difficult to
obtain for HAR datasets. Progress is starting to be
made in the unsupervised setting, in the form of
deep HAR clustering models, which can assign labels
to data without having been given any labels to
train on, but there are problems with evaluating
deep HAR clustering models, which makes assessing
the field and devising new methods difficult. In
this paper, we highlight several distinct problems
with how deep HAR clustering models are evaluated,
describing these problems in detail and conducting
careful experiments to explicate the effect that
they can have on results. Additionally, we present a
new deep clustering model for HAR. When tested under
our proposed settings, our model performs better
than (or on par with) existing models, while also
being more efficient and scalable by avoiding the
need for an autoencoder.