# Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR

arXiv: https://arxiv.org/abs/2402.14427 · HTML: https://mlsanity.com/paper/2402.14427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images](https://mlsanity.com/paper/2006.03630/) (2020)
- [PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation](https://mlsanity.com/paper/2302.00391/) (2023)

## Built on by (most cited first, 3 of 3)

- [Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUs](https://mlsanity.com/paper/2406.01316/) (2024)
- [OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models](https://mlsanity.com/paper/2501.00432/) (2024)
- [SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models](https://mlsanity.com/paper/2507.06405/) (2025)

## Abstract (arXiv)

In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physical sensors itself can be cost-prohibitive, time-consuming. To address this critical need, we introduce Text-to-Pressure (T2P), a framework designed to generate extensive ground pressure sequences from textual descriptions of human activities using deep learning techniques. We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps. We achieved comparable performance on the consistency between text and generated motion with an R squared value of 0.722, Masked R squared value of 0.892, and FID score of 1.83. Additionally, we trained a HAR model with the the synthesized data and evaluated it on pressure dynamics collected by a real pressure sensor which is on par with a model trained on only real data. Combining both real and synthesized training data increases the overall macro F1 score by 5.9 percent.