  User dependence remains one of the most difficult general problems in Human
Activity Recognition (HAR), in particular when using wearable sensors. This is
due to the huge variability of the way different people execute even the
simplest actions. In addition, detailed sensor fixtures and placement will be
different for different people or even at different times for the same users.
In theory, the problem can be solved by a large enough data set. However,
recording data sets that capture the entire diversity of complex activity sets
is seldom practicable. Instead, models are needed that focus on features that
are invariant across users. To this end, we present an adversarial
subject-independent feature extraction method with the maximum mean discrepancy
(MMD) regularization for human activity recognition. The proposed model is
capable of learning a subject-independent embedding feature representation from
multiple subjects datasets and generalizing it to unseen target subjects. The
proposed network is based on the adversarial encoder-decoder structure with the
MMD realign the data distribution over multiple subjects. Experimental results
show that the proposed method not only outperforms state-of-the-art methods
over the four real-world datasets but also improves the subject generalization
effectively. We evaluate the method on well-known public data sets showing that
it significantly improves user-independent performance and reduces variance in
results.
