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Gait Neural Network for Human-Exoskeleton Interaction.

Bin Fang¹, Quan Zhou², Fuchun Sun¹

¹Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.

Frontiers in Neurorobotics

|November 16, 2020

Summary

This summary is machine-generated.

This study introduces a gait neural network (GNN) to improve robotic exoskeleton synchronization with human movement. The GNN model enhances human-exoskeleton interaction by accurately recognizing and predicting user gait patterns.

Keywords:

exoskeleton gait neural network gait recognition interaction prediction temporal convolutional network

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Area of Science:

Robotics
Biomedical Engineering
Artificial Intelligence

Background:

Robotic exoskeletons aim to enhance human capabilities but currently suffer from poor synchronization with user movements.
Optimizing human-exoskeleton interaction is crucial for effective and intuitive device operation.
Existing methods for gait analysis in exoskeleton control are insufficient for real-time adaptation.

Purpose of the Study:

To propose a novel gait recognition and prediction model, the gait neural network (GNN), for enhanced human-exoskeleton synchronization.
To leverage temporal convolutional networks and a unique GNN structure to utilize historical sensor data effectively.
To improve the seamless integration and intuitive control of robotic exoskeletons.

Main Methods:

Development of the gait neural network (GNN) model based on temporal convolutional networks.
Implementation of a GNN structure comprising an intermediate network, a target network, and a recognition/prediction module.
Utilizing historical sensor data through the novel GNN architecture for gait analysis.

Main Results:

The GNN model demonstrated high effectiveness in gait recognition and prediction.
Performance evaluation on the HuGaDB dataset and custom inertial motion capture data confirmed the model's capabilities.
The proposed GNN approach achieved superior performance compared to existing methods in human-exoskeleton synchronization.

Conclusions:

The proposed gait neural network (GNN) significantly improves synchronization between humans and robotic exoskeletons.
The GNN model offers a robust solution for real-time gait recognition and prediction, enhancing device usability.
This advancement paves the way for more intuitive and effective robotic exoskeleton applications in daily life and rehabilitation.