发表机构
University of Science and Technology of China（中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单设备压力监测范围有限的问题，提出端到端网络MDP-Net，结合MoE框架的多模态融合机制，构建MDP数据集，实现跨设备压力数据的3D人体网格估计，关节位置误差为12.6cm。
AI 中文摘要
人体姿态监测在康复评估、人机交互等领域至关重要，基于压力的人体姿态监测因具备隐私保护特性，成为非侵入式感知的主要方法。但现有方法通常局限于单设备，限制了有效监测范围。为解决这一局限，我们提出MDP-Net，这是一种端到端网络，能够直接从跨设备的时序压力数据中估计人体网格。我们引入受混合专家（Mixture of Experts，MoE）框架启发的多模态融合机制，实现跨设备压力信息的有效互补与增强。为支持MDP-Net的训练与评估，我们构建了高质量多设备时序压力数据集MDP，其包含2D/3D关节、人体网格等多种姿态标签。实验结果表明，MDP-Net在MDP数据集上的关节位置误差为12.6厘米，这些结果证明融合多设备压力信息是日常人体姿态监测的有效且有前景的新方案。
英文摘要
Human pose monitoring is crucial in fields such as rehabilitation assessment and human-computer interaction. Due to its privacy-preserving nature, pressure-based human pose monitoring has become a primary approach for unobtrusive sensing. However, existing methods are generally limited to a single device, which restricts the effective monitoring range. To address this limitation, we propose MDP-Net, an end-to-end network capable of directly estimating human meshes from temporal pressure data across multiple devices. We introduce a multimodal fusion mechanism inspired by the Mixture of Experts (MoE) framework to achieve effective complementarity and enhancement of cross-device pressure information. To support the training and evaluation of MDP-Net, we constructed MDP, a high-quality multi-device temporal pressure dataset that includes various pose labels such as 2D/3D joints and human meshes. Experimental results demonstrate that MDP-Net achieves a joint position error of 12.6 cm on the MDP dataset. These results prove that fusing multi-device pressure information is an effective and promising new solution for daily human pose monitoring.
