On the relationship between features extracted from EMG and force for constant and dynamic protocols

Area of Science:

• Biomedical Engineering
• Neuroscience
• Biomechanics

Background:

• Understanding the electromyography (EMG) and force relationship is crucial for various applications.
• Existing analysis methods may lack the comprehensive features needed for detailed EMG-force correlation studies.

Purpose of the Study:

• To characterize the relationship between electromyography (EMG) and force using a novel analysis tool.
• To evaluate the utility of the developed tool in analyzing EMG-force dynamics.

Main Methods:

• Developed a specialized analysis tool for simultaneous time-domain feature extraction from EMG signals.
• Collected EMG and force data from 15 subjects across eight experimental protocols.
• Utilized linear, quadratic, and exponential models for feature evaluation.

Main Results:

• Statistical EMG features (kurtosis, skewness) showed low sensitivity to dynamic force protocols.
• Amplitude-based EMG signal features demonstrated higher appropriateness for constant force protocols.
• The developed tool enables visualization of EMG features against force signals.

Conclusions:

• The developed analysis tool offers valuable insights into the EMG-force relationship.
• Amplitude features are more suitable for constant force protocols, while statistical features are less sensitive dynamically.
• Findings can inform the development of EMG signal simulators and automated analysis systems.