  Unsupervised user adaptation aligns the feature distributions of the data
from training users and the new user, so a well-trained wearable human activity
recognition (WHAR) model can be well adapted to the new user. With the
development of wearable sensors, multiple wearable sensors based WHAR is
gaining more and more attention. In order to address the challenge that the
transferabilities of different sensors are different, we propose SALIENCE
(unsupervised user adaptation model for multiple wearable sensors based human
activity recognition) model. It aligns the data of each sensor separately to
achieve local alignment, while uniformly aligning the data of all sensors to
ensure global alignment. In addition, an attention mechanism is proposed to
focus the activity classifier of SALIENCE on the sensors with strong feature
discrimination and well distribution alignment. Experiments are conducted on
two public WHAR datasets, and the experimental results show that our model can
yield a competitive performance.
