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Related Experiment Video

Updated: Jul 24, 2025

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against

Jiale Gong^1,2, Ziyang Li¹, Mingzhu Chen³

¹Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This study enhances human spatial positioning using improved inertial measurement unit (IMU) and pressure insole sensors. The new method offers higher accuracy for real-time indoor positioning and path tracing across various applications.

Keywords:

WSN error compensation motion capture plantar pressure positioning

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Last Updated: Jul 24, 2025

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Published on: August 30, 2016

Area of Science:

Robotics and Human-Computer Interaction
Sensor Fusion and Signal Processing
Biomedical Engineering and Applied Physiology

Background:

Existing MEMS-based human positioning methods suffer from accuracy limitations, poor real-time performance, and scene-specific constraints.
Accurate human spatial positioning is crucial for diverse applications including industrial, medical, and rescue operations.
Improving the accuracy of Inertial Measurement Unit (IMU)-based foot localization and path tracing remains a significant challenge.

Purpose of the Study:

To enhance a planar spatial human positioning method using high-resolution pressure insoles and IMU sensors.
To develop a real-time position compensation method adaptable to different walking modes.
To improve the 3D accuracy of practical human positioning through multi-sensor data fusion.

Main Methods:

Integration of two high-resolution pressure insoles with a wireless sensor network (WSN) system comprising 12 IMUs.
Implementation of multi-sensor data fusion for dynamic recognition and automatic matching of compensation values.
Real-time spatial-position calculation of the touchdown foot during locomotion.

Main Results:

The proposed method demonstrated superior real-time indoor positioning and path-tracking accuracy compared to three traditional methods.
Dynamic recognition and compensation for five distinct walking modes were successfully implemented.
Enhanced 3D positioning accuracy was achieved through the fusion of pressure insole and IMU data.

Conclusions:

The improved human positioning method offers significantly higher accuracy and real-time performance for indoor navigation and tracking.
The methodology shows potential for broad and effective application in industrial, medical, and rescue scenarios.
This approach advances the field of wearable sensing for accurate human motion analysis.