  One of the primary challenges in the field of human activity recognition
(HAR) is the lack of large labeled datasets. This hinders the development of
robust and generalizable models. Recently, cross modality transfer approaches
have been explored that can alleviate the problem of data scarcity. These
approaches convert existing datasets from a source modality, such as video, to
a target modality (IMU). With the emergence of generative AI models such as
large language models (LLMs) and text-driven motion synthesis models, language
has become a promising source data modality as well as shown in proof of
concepts such as IMUGPT. In this work, we conduct a large-scale evaluation of
language-based cross modality transfer to determine their effectiveness for
HAR. Based on this study, we introduce two new extensions for IMUGPT that
enhance its use for practical HAR application scenarios: a motion filter
capable of filtering out irrelevant motion sequences to ensure the relevance of
the generated virtual IMU data, and a set of metrics that measure the diversity
of the generated data facilitating the determination of when to stop generating
virtual IMU data for both effective and efficient processing. We demonstrate
that our diversity metrics can reduce the effort needed for the generation of
virtual IMU data by at least 50%, which open up IMUGPT for practical use cases
beyond a mere proof of concept.
