[Construction and analysis of muscle functional network for exoskeleton robot]

Area of Science:

• Robotics
• Biomedical Engineering
• Neuroscience

Background:

• Exoskeleton nursing robots require understanding wearer intent for effective human-machine collaboration.
• Analyzing surface electromyography (EMG) is crucial for decoding wearer intentions in human-machine systems.
• The spatial distribution and relationships within EMG signals are complex and require advanced analysis.

Purpose of the Study:

• To develop a method for analyzing upper limb muscle functional networks using EMG data.
• To identify key muscle groups and movement stages in exoskeleton wearers during patient handling.
• To enhance the control and responsiveness of exoskeleton nursing robots.

Main Methods:

• Abstracting the upper limb muscle system into a muscle functional network.
• Utilizing mutual information to analyze correlations between EMG channels.
• Applying network node characteristic indices and node contraction methods to identify key muscle groups and movement stages.

Main Results:

• Successfully established muscle functional networks from upper limb EMG data.
• Identified key muscle groups reflecting wearer movement intentions.
• Effectively distinguished different stages of the patient moving process using the muscle functional network.
• Demonstrated efficient determination of myoelectric collection locations.

Conclusions:

• The muscle functional network approach provides a novel method for analyzing EMG signals in exoskeleton applications.
• This method enables better decoding of neural control signals for upper limb motion.
• The findings can improve the design and control of human-machine co-drive systems like exoskeleton nursing robots.