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Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

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Published on: January 9, 2016

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion.

Jiyuan Song^1,2, Aibin Zhu^1,2, Yao Tu^1,2

¹Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|January 23, 2020

Summary

This study developed a human motion recognition system using lower extremity acceleration and plantar data for intelligent exoskeleton control. The system accurately identifies gait stages, crucial for advanced hybrid control strategies.

Keywords:

inertial sensor lower limb assisted exoskeleton motion pattern recognition neural network plantar pressure

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Area of Science:

Biomechanics
Robotics
Artificial Intelligence

Background:

Intelligent hybrid control of exoskeletons requires rapid recognition of the wearer's gait stage.
Existing methods may lack accuracy in recognizing complex or mixed motion patterns.

Purpose of the Study:

To develop a human body mixed motion pattern recognition technology for intelligent exoskeleton control.
To establish a nonlinear mapping model between multi-source feature parameters and motion states.

Main Methods:

Acquired lower extremity acceleration and plantar data.
Extracted time, frequency, and energy domain features from multi-source motion information.
Utilized a distance-based feature screening method for optimal feature selection.
Developed a multi-layer BP (back propagation) neural network for motion state recognition.

Main Results:

Achieved up to 98.28% recognition accuracy in single motion modes.
Obtained 92.7% and 97.4% recognition accuracy in mixed motion modes.
Verified the feasibility and effectiveness of the developed model.

Conclusions:

The proposed multi-source feature-based neural network model effectively recognizes human gait stages.
This technology is vital for enhancing the performance and safety of intelligent exoskeleton systems.