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.

翻译：在人类活动识别（HAR）中，标注数据有限性构成了重大挑战。受生成式人工智能最新进展（包括大型语言模型（LLMs）和运动合成模型）的启发，我们认为生成式AI能够通过从文本描述中自主生成虚拟IMU数据来解决这一数据稀缺问题。此外，我们重点提出了若干有前景的研究路径，这些路径可通过生成式AI惠及该领域，包括生成基准数据集、开发HAR专用基础模型、探索HAR内的层次结构、分解复杂活动，以及在健康感知和活动摘要中的应用。