In human activity recognition (HAR), the limited availability of annotated
data presents a significant challenge. Drawing inspiration from the latest
advancements in generative AI, including Large Language Models (LLMs) and
motion synthesis models, we believe that generative AI can address this data
scarcity by autonomously generating virtual IMU data from text descriptions.
Beyond this, we spotlight several promising research pathways that could
benefit from generative AI for the community, including the generating
benchmark datasets, the development of foundational models specific to HAR, the
exploration of hierarchical structures within HAR, breaking down complex
activities, and applications in health sensing and activity summarization.

通过生成式人工智能从文本描述中自动生成虚拟 IMU 数据，人类活动识别（HAR）领域能够解决有限标注数据的问题。此外，还探讨了生成式人工智能在生成基准数据集、特定于 HAR 的模型开发、HAR 中的层次结构探索、复杂活动分解以及健康感知和活动总结等领域有着前景的研究方向。

关于生成式基础模型对人类活动识别的好处

On the Benefit of Generative Foundation Models for Human Activity  Recognition

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.

通过聊天 GPT 生成不同的文本描述活动的文字描述，通过运动合成模型 T2M-GPT 生成 3D 人体运动序列，然后转换成流的模拟 IMU 数据，将其与部分真实 IMU 数据结合训练 HAR 模型可以显著提高其性能