Analyzing and Modeling the Dynamic Electrical Characteristics of Nanocomposite Large-Range Strain Gauges

Area of Science:

• Biomechanics
• Sensor Technology
• Materials Science

Background:

• Flexible high-deflection strain gauges offer cost-effective human biomechanical deformation measurement.
• Interpreting these sensors during dynamic motion is challenging due to material viscoelasticity and resistance spikes.
• Existing models often fail to accurately capture dynamic strain-resistance relationships.

Purpose of the Study:

• To develop a novel model for accurately capturing the dynamic strain-resistance relationship of flexible high-deflection strain sensors.
• To address the limitations of quasi-static models in dynamic biomechanical applications.
• To enable reliable strain extraction from resistance measurements during cyclical movements.

Main Methods:

• Developed a four-part forward model (quasi-static linear, spike magnitude, long-term creep decay, short-term decay) to predict resistance from strain.
• Created and calibrated an inverse model to predict strain from resistance data.
• Validated models using cyclical movement data.

Main Results:

• The forward model achieved an R-squared value of 0.90 in predicting resistance output.
• The inverse model accurately predicted key strain characteristics with a percent error as low as 0.5%.
• The models successfully captured resistance spikes during strain path changes.

Conclusions:

• The developed models provide accurate interpretation of high-deflection strain sensors during dynamic movements.
• This enables new functionalities for wearable sensors in biomechanical modeling and analysis.
• The findings advance the use of flexible strain sensors for real-time biomechanical tracking.