# 基于鞋垫传感器融合的鲁棒实时单目人体姿态估计

> 原文链接：https://robotics.hellobike.com/papers/2026-10-14-2610-19494
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：针对单目实时姿态在遮挡或短时离框下失准，融合智能鞋垫足底压力与加速度，在多模态数据集和三类视觉骨干上验证。

# 基于鞋垫传感器融合的鲁棒实时单目人体姿态估计

::: v-pre
> 原文链接：[Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion](https://arxiv.org/abs/2610.19494)（arXiv 2610.19494 · 发布日期：2026-10-14）

**Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion**

针对单目实时姿态在遮挡或短时离框下失准，融合智能鞋垫足底压力与加速度，在多模态数据集和三类视觉骨干上验证。

## 核心要点

- 下肢遮挡或短时画面外会使纯视觉难以恢复合理下肢运动和可靠足地接触。
- 采集同步多模态数据，提出部位解耦时序融合，将足部传感特征补入下肢分支。
- 轻量协调器在上下肢时序表示间交换信息，以保持全身姿态一致性。
- 在2D关键点、3D地标和SMPL人体网格恢复骨干上，精度、平滑性和步态测量均有提升。

**验证范围与局限**：验证范围为所采集的同步多模态数据集，以及遮挡和短时视觉丢失条件下的三类实时视觉骨干。

## 英文摘要 (Abstract)

Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods have achieved impressive accuracy, fundamental challenges persist, particularly when visual observations are incomplete, such as during lower-body occlusion or short out-of-frame intervals. In these challenging but common scenarios, vision-only systems often struggle to reconstruct physically plausible lower-limb motion and reliable foot-ground contact. Due to the highly unobtrusive and wearable nature of smart insoles, this paper investigates whether simple plantar pressure and acceleration sensors can effectively resolve these visually induced limitations. To systematically investigate this, we first collect a synchronized multimodal dataset and then propose a part-decoupled temporal fusion framework. The proposed model fuses foot-sensing features into the lower-body branch to compensate for incomplete visual evidence, while a lightweight coordinator exchanges information between the upper- and lower-body temporal representations to maintain whole-body consistency. The framework is evaluated with three representative real-time visual backbones based on 2D keypoints, 3D landmarks, and SMPL-based human mesh recovery. We evaluate our framework on three representative real-time visual backbones. Quantitative experiments demonstrate that incorporating simple foot sensors consistently improves pose accuracy, motion smoothness, and gait-related measurements over vision-only baselines, with the most significant gains observed under occlusion and short visual-dropout conditions. Ultimately, our results demonstrate that unobtrusive foot sensing provides vital and complementary physical observability when camera observations are temporarily unavailable.

作者：Liang Yu, Tao Zhu, Xiangqing Lu, Wenjian Luo, Liming Chen
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## 原始参考来源 (Sources)

- [arXiv 2610.19494：Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion](https://arxiv.org/abs/2610.19494)