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A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Sujiao Li^1,2,3, Lei Zhang^1,2, Qiaoling Meng^1,2,3

¹Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

Bioengineering (Basel, Switzerland)

|December 23, 2023

Summary

This summary is machine-generated.

This study introduces a novel real-time control method for upper limb exoskeleton robots, enhancing stroke rehabilitation. The system uses active torque prediction and adjustable assistance for personalized and effective patient recovery.

Keywords:

active torque prediction model real-time control rehabilitation exoskeleton sEMG

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Area of Science:

Biomedical Engineering
Robotics
Neurorehabilitation

Background:

Exoskeleton robots are crucial for stroke patient rehabilitation.
Training with active movement intentions improves rehabilitation outcomes.
Individualized and precise rehabilitation is essential for stroke recovery.

Purpose of the Study:

To propose a real-time control method for upper limb exoskeletons using an active torque prediction model.
To enable individualized and precise rehabilitation through an adjustable assist ratio.
To enhance human-computer interaction in exoskeleton-assisted therapy.

Main Methods:

Developed an active torque prediction model for upper limb exoskeletons.
Utilized electromyography (EMG) signals and elbow angle as control inputs.
Trained a Backpropagation (BP) neural network for feature extraction and torque prediction.
Implemented the model on both PC and embedded systems.

Main Results:

The active torque prediction model demonstrated high accuracy on PC and embedded systems.
Achieved a Root Mean Square Error (RMSE) of 0.1956 N·m and 94.98% accuracy on the embedded system.
The real-time control system exhibited a low delay of 40 ms.

Conclusions:

The proposed method offers an accurate and efficient real-time control for upper limb exoskeletons.
The adjustable assist ratio allows for personalized rehabilitation.
The low system delay enhances adaptability and effectiveness in human-computer interactions for stroke rehabilitation.