On the Benefit of Generative Foundation Models for Human Activity
  Recognition
Zikang Leng Hyeokhyen Kwon Thomas Plötz
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.