# SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data

arXiv: https://arxiv.org/abs/2509.12994 · HTML: https://mlsanity.com/paper/2509.12994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 16 of 16)

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- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
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- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs](https://mlsanity.com/paper/2311.09774/) (2023)
- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition](https://mlsanity.com/paper/2410.10624/) (2024)
- [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](https://mlsanity.com/paper/2004.01166/) (2020)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors](https://mlsanity.com/paper/2410.20034/) (2024)
- [Posture Prediction for Healthy Sitting using a Smart Chair](https://mlsanity.com/paper/2201.02615/) (2022)

## Abstract (arXiv)

Poor sitting posture is a critical yet often overlooked factor contributing to long-term musculoskeletal disorders and physiological dysfunctions. Existing sitting posture monitoring systems, although leveraging visual, IMU, or pressure-based modalities, often suffer from coarse-grained recognition and lack the semantic expressiveness necessary for personalized feedback. In this paper, we propose SitLLM, a lightweight multimodal framework that integrates flexible pressure sensing with large language models (LLMs) to enable fine-grained posture understanding and personalized health-oriented response generation. SitLLM comprises three key components: (1) a textitGaussian-Robust Sensor Embedding Module that partitions pressure maps into spatial patches and injects local noise perturbations for robust feature extraction; (2) a textitPrompt-Driven Cross-Modal Alignment Module that reprograms sensor embeddings into the LLM's semantic space via multi-head cross-attention using the pre-trained vocabulary embeddings; and (3) a textitMulti-Context Prompt Module that fuses feature-level, structure-level, statistical-level, and semantic-level contextual information to guide instruction comprehension.
