Computational modeling improves sensor design and reduces calibration challenges in stretch sensors, indicating enhanced performance.
A stretch sensor is a device that attaches to objects and measures the amount by which they deform. These sensors have shown great promise as an alternative to vision-based motion-capture systems, and for robotic sensing. Currently, they are generally limited to linear designs, and require a somewhat challenging calibration process. Our goal is to enable inverse design of such sensors, and to largely eliminate the calibration process. To this end, we introduce an accurate, differentiable simulator for capacitive stretch sensors, that treats both the elasto- and electro -static parts of the system. Differentiability allows optimizing the geometry of the sensor in order to improve its design for specific applications. We demonstrate the accuracy of our simulator and the effectiveness of our sensor optimization process for various use cases, such as human interfaces and robotics.
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Gjoka et al. (2025) studied this question.
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