  The use of supervised learning for Human Activity Recognition (HAR) on mobile
devices leads to strong classification performances. Such an approach, however,
requires large amounts of labeled data, both for the initial training of the
models and for their customization on specific clients (whose data often differ
greatly from the training data). This is actually impractical to obtain due to
the costs, intrusiveness, and time-consuming nature of data annotation.
Moreover, even with the help of a significant amount of labeled data, model
deployment on heterogeneous clients faces difficulties in generalizing well on
unseen data. Other domains, like Computer Vision or Natural Language
Processing, have proposed the notion of pre-trained models, leveraging large
corpora, to reduce the need for annotated data and better manage heterogeneity.
This promising approach has not been implemented in the HAR domain so far
because of the lack of public datasets of sufficient size. In this paper, we
propose a novel strategy to combine publicly available datasets with the goal
of learning a generalized HAR model that can be fine-tuned using a limited
amount of labeled data on an unseen target domain. Our experimental evaluation,
which includes experimenting with different state-of-the-art neural network
architectures, shows that combining public datasets can significantly reduce
the number of labeled samples required to achieve satisfactory performance on
an unseen target domain.

Computer Vision and Pattern Recognition;Machine Learning