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

2025-05-01

AI Summary

Existing 3D human pose estimation approaches face critical limitations in clinical rehabilitation, sports injury prevention, and long-term health monitoring—particularly regarding cost, user comfort, and privacy. Vision-based methods compromise privacy, while high-fidelity wearable systems suffer from invasiveness and expense. Method: We propose a low-cost, unobtrusive, and privacy-preserving paradigm based on mass-producible e-textile smart insoles priced under USD 1. Each insole integrates a flexible pressure-sensing array and a miniature IMU to concurrently capture plantar pressure distribution, triaxial acceleration, and angular velocity. We further introduce first- and second-order temporal derivative features to enhance multimodal time-series modeling within a Transformer architecture. Contribution/Results: Experimental evaluation demonstrates robust accuracy across diverse pose estimation tasks. Our approach overcomes key bottlenecks of conventional methods—achieving superior cost-efficiency, wearability, and data privacy—while enabling practical deployment in both clinical and daily-life settings.

Technology Category

Intelligent Robots: State EstimationComputer Vision: Biometrics, Face, Gesture & PoseHumans and AI: Interaction Techniques and Devices

Application Category

User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationSecurity and Privacy: Large-scale security measurementsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Estimating 3D human pose using low-cost insole sensors
• Overcoming motion tracking limitations with multimodal data
• Enabling affordable health monitoring and rehabilitation applications

Innovation

• Low-cost e-textile insoles with IMUs
• Transformer model for temporal feature extraction
• Multimodal data fusion for motion recognition