  Human Activity Recognition (HAR) from wearable sensor data identifies
movements or activities in unconstrained environments. HAR is a challenging
problem as it presents great variability across subjects. Obtaining large
amounts of labelled data is not straightforward, since wearable sensor signals
are not easy to label upon simple human inspection. In our work, we propose the
use of neural networks for the generation of realistic signals and features
using human activity monocular videos. We show how these generated features and
signals can be utilized, instead of their real counterparts, to train HAR
models that can recognize activities using signals obtained with wearable
sensors. To prove the validity of our methods, we perform experiments on an
activity recognition dataset created for the improvement of industrial work
safety. We show that our model is able to realistically generate virtual sensor
signals and features usable to train a HAR classifier with comparable
performance as the one trained using real sensor data. Our results enable the
use of available, labelled video data for training HAR models to classify
signals from wearable sensors.
