  The development of robust, generalized models in human activity recognition
(HAR) has been hindered by the scarcity of large-scale, labeled data sets.
Recent work has shown that virtual IMU data extracted from videos using
computer vision techniques can lead to substantial performance improvements
when training HAR models combined with small portions of real IMU data.
Inspired by recent advances in motion synthesis from textual descriptions and
connecting Large Language Models (LLMs) to various AI models, we introduce an
automated pipeline that first uses ChatGPT to generate diverse textual
descriptions of activities. These textual descriptions are then used to
generate 3D human motion sequences via a motion synthesis model, T2M-GPT, and
later converted to streams of virtual IMU data. We benchmarked our approach on
three HAR datasets (RealWorld, PAMAP2, and USC-HAD) and demonstrate that the
use of virtual IMU training data generated using our new approach leads to
significantly improved HAR model performance compared to only using real IMU
data. Our approach contributes to the growing field of cross-modality transfer
methods and illustrate how HAR models can be improved through the generation of
virtual training data that do not require any manual effort.
