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

TL;DR

P2P-Insole introduces an affordable, lightweight insole sensor system utilizing foot pressure and motion data with Transformer models for accurate 3D human pose estimation, suitable for health and rehabilitation applications.

Contribution

The paper presents a novel low-cost insole sensor system combined with Transformer-based processing for efficient 3D human pose estimation from foot data.

Findings

Demonstrates robustness across various posture estimation tasks.

Achieves improved accuracy with multimodal sensor data.

Cost-effective design suitable for large-scale deployment.

Abstract

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.