Example of a custom-built breadboard setup used to supply constant current to the sensor, condition and amplify the signal, and interface with a computer for data logging and plotting.

Deformation Sensing in Soft Bio-Surrogate Materials

Accurate measurement of deformations occurring within or on soft materials has recently generated interest for its benefits to the fields of soft robotics and wearable biomedical sensors.

Deformation sensing in and on soft materials has garnered increased interest with the advancement of emerging technologies such as soft robotics, wearable computing, and biomedical applications. These applications have a need for quantification of stretching-contraction deformations along a single-axis as well as multi-directional deformation quantifications (i.e. bending, pressure, membrane stretch, planar and torsional shear).

These measurement devices must conform to the movements of the device or component under test, which can be complex, involving multiple degrees-of-freedom and dynamic rates, while closely matching the mechanical properties of the system they are embedded in, such as skin, tissue, textiles, and soft actuators. For example, soft actuators are increasingly used in the fields of bio-fidelic robotics and aerospace, however, there is an absence of reliable positional and force feedback, which is necessary to provide a soft touch as well as accurate and controllable behavior. Soft sensors are being implemented to provide this feedback, however, embedding them without hindering the actuator’s functionality has been a significant challenge.

Traditional strain gages, typically made of a resistive metal alloy element, cannot measure large strains (typically limited to strains < 1%). They are well suited to measure strain on stiffer materials (e.g., metals, composites, plastics), but cannot be used for strain measurements on soft materials, whose stiffness is much lower than the gage itself and whose strains may be greater than 20%, sometimes 250%+. In designing a soft strain sensor, the following requirements (application dependent) need to be considered:

  1. Reliable, accurate quantification of small, intermediate, and large strains (20% +);

  2. Type of quantification desired (i.e. normal or shear strain due to axial, bending, pressure, shear, or torsional loading);

  3. Impedance matching of the sensor with the material system for accuracy as well as unimpeded movement;

  4. Biomechanically relevant strain rates and frequency responses.

The objective of this research was to develop and characterize materials and sensors for measuring normal and shear strains in soft materials. Of particular interest was developing impedance matched soft strain sensors for use in anatomically correct, bio-surrogate injury assessments in visually obscured threat protection applications.

There are three types of electromechanical, soft sensors: piezoresistive, piezoelectric, and capacitive sensors.

Piezoresistive sensors exhibit a change of resistance under an applied deformation. The resistance change typically occurs as a result of deformation induced changes in the sensing element’s physical dimensions.

Piezoelectric sensors measure strain by transducing a stress-induced electric field. They are susceptible to temperature changes, however, and flexible piezoelectric materials, such as polymers, have a low piezoelectric coefficient.

Capacitive type sensors measure change in capacitance between a set or sets of embedded electrodes. Although capacitive type sensors can accommodate large deformations, they typically have very low sensitivity (gage factors < 1). For these reasons, the focus of this research was on piezoresistive type sensors.

This work was done by Christopher C. Rudolph for the Naval Research Laboratory. For more information, download the Technical Support Package (free white paper) here under the Sensors category. NRL-0076

This Brief includes a Technical Support Package (TSP).
Deformation Sensing in Soft Bio-Surrogate Materials

(reference NRL-0076) is currently available for download from the TSP library.

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