用脚底压力与运动传感器低成本实现3D人体姿态估计
P2P-Insole: Human Pose Estimation Using Foot Pressure Distribution and Motion Sensors
- 融合足底压力、加速度和旋转数据，用Transformer建模时序特征
- 在多种姿势估计任务中误差小，实测性能稳定可靠
- 适合康复、防伤等场景，成本低于1美元，易规模化应用
本文提出P2P-Insole，一种基于嵌入惯性测量单元（IMU）的鞋垫式传感器系统，用于低成本估算和可视化3D人体骨骼数据。每片鞋垫采用电子织物工艺制造，成本低于1美元，显著低于商用方案，适合大规模生产。系统利用足底压力分布、加速度和旋转数据，克服传统方法局限，提供轻量、低侵入且保护隐私的解决方案。通过引入一阶和二阶导数增强输入流，结合Transformer模型高效提取时序特征。融合多模态信息（如加速度计与角速度）显著提升复杂动作识别精度。实验验证了方法在多种姿态估计任务中的鲁棒性，误差指标表现优异。该工作为康复、损伤预防与健康监测等场景提供实用化基础，并可通过传感器优化与数据集扩展持续改进。
原文摘要 · Abstract (English)
This work presents P2P-Insole, a low-cost approach for estimating and visualizing 3D human skeletal data using insole-type sensors integrated with IMUs. Each insole, fabricated with e-textile garment techniques, costs under USD 1, making it significantly cheaper than commercial alternatives and ideal for large-scale production. Our approach uses foot pressure distribution, acceleration, and rotation data to overcome limitations, providing a lightweight, minimally intrusive, and privacy-aware solution. The system employs a Transformer model for efficient temporal feature extraction, enriched by first and second derivatives in the input stream. Including multimodal information, such as accelerometers and rotational measurements, improves the accuracy of complex motion pattern recognition. These facts are demonstrated experimentally, while error metrics show the robustness of the approach in various posture estimation tasks. This work could be the foundation for a low-cost, practical application in rehabilitation, injury prevention, and health monitoring while enabling further development through sensor optimization and expanded datasets.
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