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.

在人类活动识别领域，缺乏大型标记数据集是主要挑战之一。为了解决数据稀缺的问题，最近开始研究跨模态迁移方法，将现有数据集从源模态（如视频）转换为目标模态（IMU）。本研究通过大规模评估语言驱动的跨模态迁移，提出了两个针对实际人类活动识别应用场景的 IMUGPT 扩展，一个是能够滤除无关动作序列以确保生成虚拟 IMU 数据的相关性的动作过滤器，另一个是一套用于测量生成数据多样性的指标，帮助确定何时停止生成虚拟 IMU 数据以实现有效和高效的处理。我们证明了我们的多样性指标可以减少生成虚拟 IMU 数据所需的工作量至少 50％，从而使 IMUGPT 在纯概念证明之外具备实际应用的价值。