用压力传感器+大模型，精准识别坐姿并生成健康建议
SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
- 将压力图分块并加噪，提升特征鲁棒性
- 通过提示词对齐传感器与语言模型语义空间
- 融合多层上下文信息，实现个性化健康反馈
不良坐姿是导致长期肌肉骨骼疾病和生理功能障碍的重要但常被忽视的因素。现有坐姿监测系统虽采用视觉、惯性传感或压力传感方式，却普遍存在识别粒度粗、缺乏语义表达能力的问题，难以提供个性化反馈。本文提出一种轻量级多模态框架 SitLLM，融合柔性压力传感与大语言模型（LLMs），实现细粒度坐姿理解与健康导向的个性化响应生成。该框架包含三个核心组件：(1) 高斯鲁棒传感器嵌入模块，将压力图划分为空间块并注入局部噪声扰动以增强特征提取鲁棒性；(2) 提示驱动跨模态对齐模块，通过多头交叉注意力机制，利用预训练词汇嵌入将传感器嵌入映射至语言模型语义空间；(3) 多上下文提示模块，融合特征级、结构级、统计级与语义级上下文信息，引导指令理解。
原文摘要 · Abstract (English)
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 \textbf{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 \textit{Gaussian-Robust Sensor Embedding Module} that partitions pressure maps into spatial patches and injects local noise perturbations for robust feature extraction; (2) a \textit{Prompt-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 \textit{Multi-Context Prompt Module} that fuses feature-level, structure-level, statistical-level, and semantic-level contextual information to guide instruction comprehension.
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