# P2P-Insole: Human Pose Estimation Using Foot Pressure Distribution and Motion Sensors

arXiv: https://arxiv.org/abs/2505.00755 · HTML: https://mlsanity.com/paper/2505.00755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 10 of 10)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Populating 3D Scenes by Learning Human-Scene Interaction](https://mlsanity.com/paper/2012.11581/) (2020)
- [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [HPERL: 3D Human Pose Estimation from RGB and LiDAR](https://mlsanity.com/paper/2010.08221/) (2020)
- [Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture](https://mlsanity.com/paper/2309.00310/) (2023)
- [MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors](https://mlsanity.com/paper/2403.17610/) (2024)
- [In-bed Pressure-based Pose Estimation using Image Space Representation Learning](https://mlsanity.com/paper/1908.08919/) (2019)
- [Human Pose Estimation from Ambiguous Pressure Recordings with Spatio-temporal Masked Transformers](https://mlsanity.com/paper/2303.05691/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Real-World Wearable Motion Reconstruction](https://mlsanity.com/paper/2607.09780/) (2026)

## Abstract (arXiv)

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.