Geometrically Optimized FDM-Printed Conductive TPU Bend Sensors for Hand Rehabilitation

Area of Science:

• Materials Science
• Robotics
• Biomedical Engineering

Background:

• Flexible resistive bend sensors are crucial for human movement monitoring in smart rehabilitation and soft robotics.
• Current limitations include high costs of metal-film sensors and performance issues with low-cost composites.

Purpose of the Study:

• To develop a cost-effective, geometrically customizable bending sensor using Fused Deposition Modeling (FDM).
• To tailor sensor performance for specific applications, such as finger-joint monitoring.

Main Methods:

• Fabrication of sensors using conductive thermoplastic polyurethane (TPU) via FDM.
• Parametric optimization of physical dimensions (trace width, layer thickness, pattern geometry).
• Electromechanical characterization and polynomial modeling for hysteresis compensation.

Main Results:

• Achieved a nominal resistance of ~44 kΩ, tailored within a 20-50 kΩ window.
• Demonstrated a negative gauge factor due to conductive pathway changes upon bending.
• Implemented a third-degree polynomial model with R² ≈ 0.90 to compensate for non-linearity and hysteresis.

Conclusions:

• The FDM-printed sensor provides a cost-effective, high-performance solution overcoming geometric limitations.
• This technology enables tailor-made wearable devices for smart rehabilitation and soft robotics.
• The developed sensor addresses the performance-cost trade-off of existing bend sensor technologies.