# On the Benefit of Generative Foundation Models for Human Activity Recognition

arXiv: https://arxiv.org/abs/2310.12085 · HTML: https://mlsanity.com/paper/2310.12085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation](https://mlsanity.com/paper/2304.10417/) (2023)
- [IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition](https://mlsanity.com/paper/2006.05675/) (2020)
- [Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data](https://mlsanity.com/paper/2211.01342/) (2022)
- [Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think](https://mlsanity.com/paper/2101.07855/) (2021)

## Built on by (most cited first, 2 of 2)

- [LLMs are Good Action Recognizers](https://mlsanity.com/paper/2404.00532/) (2024)
- [AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection](https://mlsanity.com/paper/2505.04660/) (2025)

## Abstract (arXiv)

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.