  Human activity recognition (HAR) using wearable sensors has benefited much
less from recent advances in Machine Learning than fields such as computer
vision and natural language processing. This is to a large extent due to the
lack of large scale repositories of labeled training data. In our research we
aim to facilitate the use of online videos, which exists in ample quantity for
most activities and are much easier to label than sensor data, to simulate
labeled wearable motion sensor data. In previous work we already demonstrate
some preliminary results in this direction focusing on very simple, activity
specific simulation models and a single sensor modality (acceleration
norm)\cite{10.1145/3341162.3345590}. In this paper we show how we can train a
regression model on generic motions for both accelerometer and gyro signals and
then apply it to videos of the target activities to generate synthetic IMU data
(acceleration and gyro norms) that can be used to train and/or improve HAR
models. We demonstrate that systems trained on simulated data generated by our
regression model can come to within around 10% of the mean F1 score of a system
trained on real sensor data. Furthermore we show that by either including a
small amount of real sensor data for model calibration or simply leveraging the
fact that (in general) we can easily generate much more simulated data from
video than we can collect in terms of real sensor data the advantage of real
sensor data can be eventually equalized.
