On the Benefit of Generative Foundation Models for Human Activity Recognition

• URL: http://arxiv.org/abs/2310.12085v1
• Date: Wed, 18 Oct 2023 16:27:06 GMT
• Title: On the Benefit of Generative Foundation Models for Human Activity Recognition
• Authors: Zikang Leng, Hyeokhyen Kwon, Thomas Pl\"otz
• Abstract summary: In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI, we believe generative AI can address this data scarcity by autonomously generating virtual IMU data from text descriptions. We spotlight several promising research pathways that could benefit from generative AI for the community.
• Score: 0.27624021966289597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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